Publikationen

Publikationsstatistik
Typ / Jahr 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995 1994 1993 1991 1989 Gesamt
Konferenzen 30 21 24 26 32 17 18 19 17 13 4 9 3 11 1 8 3 3 3 2 3 1 0 0 268
Zeitschriften 1 2 2 0 3 1 0 1 0 2 2 2 1 0 2 1 1 0 2 0 0 1 0 0 24
In Büchern 1 0 0 0 3 0 3 0 1 1 0 1 0 0 2 1 2 0 1 3 0 0 0 0 19
Editor von Büchern oder Zeitschriften 0 0 0 0 1 1 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 5
Preprints 2 3 5 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 13
Andere 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 1 0 0 0 0 1 1 5
Gesamt 34 26 31 28 40 19 21 20 19 17 6 12 6 11 5 11 6 4 6 5 3 2 1 1 334

Preprints

Jesús Muñoz Morcillo, Florian Faion, Antonio Zea, Uwe D. Hanebeck, Caroline Y. Robertson-von Trotha,
e-Installation: Synesthetic Documentation of Media Art via Telepresence Technologies
arXiv preprint: Other Computer Science (cs.OH), 2014.
URL
Author : Jesús Muñoz Morcillo, Florian Faion, Antonio Zea, Uwe D. Hanebeck, Caroline Y. Robertson-von Trotha
Title : e-Installation: Synesthetic Documentation of Media Art via Telepresence Technologies
In : arXiv preprint: Other Computer Science (cs.OH)
Address :
Date : 2014
Abstract
In this paper, a new synesthetic documentation method that contributes to
media art conservation is presented. This new method is called e-Installation in analogy to the
idea of the e-Book as the electronic version of a real book. An e-Installation is a virtualized
media artwork that reproduces all synesthesia, interaction, and meaning levels of the artwork.
Advanced 3D modeling and telepresence technologies with a very high level of immersion allow the virtual
re-enactment of works of media art that are no longer performable or rarely exhibited. The virtual
re-enactment of a media artwork can be designed with a scalable level of complexity depending on
whether it addresses professionals such as curators, art restorers, and art theorists or the general public.
An e-Installation is independent from the artwork's physical location and can be accessed via head-mounted
display or similar data goggles, computer browser, or even mobile devices. In combination with informational and preventive
conservation measures, the e-Installation offers an intermediate and long-term solution to archive, disseminate,
and pass down the milestones of media art history as a synesthetic documentation when the original work can no
longer be repaired or exhibited in its full function.
Gerhard Kurz, Igor Gilitschenski, Uwe D. Hanebeck,
Efficient Evaluation of the Probability Density Function of a Wrapped Normal Distribution
arXiv preprint: Computation (stat.CO), 2014.
URL
Author : Gerhard Kurz, Igor Gilitschenski, Uwe D. Hanebeck
Title : Efficient Evaluation of the Probability Density Function of a Wrapped Normal Distribution
In : arXiv preprint: Computation (stat.CO)
Address :
Date : 2014
Abstract
The wrapped normal distribution arises when a the density of a
one-dimensional normal distribution is wrapped around the circle infinitely
many times. At first look, evaluation of its probability density function appears
tedious as an infinite series is involved. In this paper, we investigate the
evaluation of two truncated series representations. As one representation performs
well for small uncertainties whereas the other performs well for large uncertainties,
we show that in all cases a small number of summands is sufficient to achieve high accuracy.
Igor Gilitschenski, Gerhard Kurz, Simon J. Julier, Uwe D. Hanebeck,
Unscented Orientation Estimation Based on the Bingham Distribution
arXiv preprint: Systems and Control (cs.SY), 2013.
URL
Author : Igor Gilitschenski, Gerhard Kurz, Simon J. Julier, Uwe D. Hanebeck
Title : Unscented Orientation Estimation Based on the Bingham Distribution
In : arXiv preprint: Systems and Control (cs.SY)
Address :
Date : 2013
Abstract
Orientation estimation for 3D objects is a common problem that is
usually tackled with traditional nonlinear filtering techniques such
as the extended Kalman filter (EKF) or the unscented Kalman filter
(UKF). Most of these techniques assume Gaussian distributions to
account for system noise and uncertain measurements. This distributional
assumption does not consider the periodic nature of pose and orientation
uncertainty. We propose a filter that considers the periodicity of
the orientation estimation problem in its distributional assumption.
This is achieved by making use of the Bingham distribution, which
is defined on the hypersphere and thus inherently more suitable to
periodic problems. Furthermore, handling of non-trivial system functions
is done using deterministic sampling in an efficient way. A deterministic
sampling scheme reminiscent of the UKF is proposed for the nonlinear
manifold of orientations. It is the first deterministic sampling
scheme that truly reflects the nonlinear manifold of the orientation.
Gerhard Kurz, Igor Gilitschenski, Simon J. Julier, Uwe D. Hanebeck,
Recursive Estimation of Orientation Based on the Bingham Distribution
arXiv preprint: Systems and Control (cs.SY), 2013.
URL
Author : Gerhard Kurz, Igor Gilitschenski, Simon J. Julier, Uwe D. Hanebeck
Title : Recursive Estimation of Orientation Based on the Bingham Distribution
In : arXiv preprint: Systems and Control (cs.SY)
Address :
Date : 2013
Abstract
Directional estimation is a common problem in many tracking applications.
Traditional filters such as the Kalman filter perform poorly because they fail to take
the periodic nature of the problem into account. We present a recursive filter for directional
data based on the Bingham distribution in two dimensions. The proposed filter can be applied
to circular filtering problems with 180 degree symmetry, i.e., rotations by 180 degrees cannot
be distinguished. It is easily implemented using standard numerical techniques and suitable for
real-time applications. The presented approach is extensible to quaternions, which allow tracking
arbitrary three-dimensional orientations. We evaluate our filter in a challenging scenario and
compare it to a traditional Kalman filtering approach.
Marcus Baum, Uwe D. Hanebeck,
Extended Object Tracking with Random Hypersurface Models
arXiv preprint: Systems and Control (cs.SY), Draft accepted for publication in IEEE Transactions on Aerospace and Electronic Systems, 2013.
URL
Author : Marcus Baum, Uwe D. Hanebeck
Title : Extended Object Tracking with Random Hypersurface Models
In : arXiv preprint: Systems and Control (cs.SY), Draft accepted for publication in IEEE Transactions on Aerospace and Electronic Systems
Address :
Date : 2013
Abstract
The Random Hypersurface Model (RHM) is introduced that allows for estimating
a shape approximation of an extended object in addition to its kinematic state.
An RHM represents the spatial extent by means of randomly scaled versions of the shape
boundary. In doing so, the shape parameters and the measurements are related via a
measurement equation that serves as the basis for a Gaussian state estimator.
Specific estimators are derived for elliptic and star-convex shapes.
Marcus Baum, Uwe D. Hanebeck,
The Kernel-SME Filter for Multiple Target Tracking
arXiv preprint: Systems and Control (cs.SY), 2012.
URL
Author : Marcus Baum, Uwe D. Hanebeck
Title : The Kernel-SME Filter for Multiple Target Tracking
In : arXiv preprint: Systems and Control (cs.SY)
Address :
Date : 2012
Abstract
We present a novel method called Kernel-SME filter for tracking multiple targets
when the association of the measurements to the targets is unknown. The method is a
further development of the Symmetric Measurement Equation (SME) filter, which removes
the data association uncertainty of the original measurement equation with the help of
a symmetric transformation. The underlying idea of the Kernel-SME filter is to construct
a symmetric transformation by means of mapping the measurements to a Gaussian mixture.
This transformation is scalable to a large number of targets and allows for deriving a
Gaussian state estimator that has a cubic time complexity in the number of targets.
Jörg Fischer, Marc Reinhardt, Uwe D. Hanebeck,
Optimal Sequence-Based Control and Estimation of Networked Linear Systems
arXiv preprint: Systems and Control (cs.SY), 2012.
URL
Author : Jörg Fischer, Marc Reinhardt, Uwe D. Hanebeck
Title : Optimal Sequence-Based Control and Estimation of Networked Linear Systems
In : arXiv preprint: Systems and Control (cs.SY)
Address :
Date : 2012
Abstract
In this paper, a unified approach to sequence-based control and estimation
of linear networked systems with multiple sensors is proposed. Time
delays and data losses in the controller-actuator-channel are compensated
by sending sequences of control inputs. The sequence-based design
paradigm is further extended to the sensor-controller-channels without
increasing the load of the network. In this context, we present a
recursive solution based on the Hypothesizing Distributed Kalman
Filter (HKF) that is included in the overall sequence-based controller
design.
Marc Peter Deisenroth, Ryan Turner, Marco F. Huber, Uwe D. Hanebeck, Carl Edward Rasmussen,
Robust Filtering and Smoothing with Gaussian Processes
arXiv preprint: Systems and Control (cs.SY), 2012.
URL
Author : Marc Peter Deisenroth, Ryan Turner, Marco F. Huber, Uwe D. Hanebeck, Carl Edward Rasmussen
Title : Robust Filtering and Smoothing with Gaussian Processes
In : arXiv preprint: Systems and Control (cs.SY)
Address :
Date : 2012
Abstract
We propose a principled algorithm for robust Bayesian filtering and
smoothing in nonlinear stochastic dynamic systems when both the transition
function and the measurement function are described by non-parametric
Gaussian process (GP) models. GPs are gaining increasing importance
in signal processing, machine learning, robotics, and control for
representing unknown system functions by posterior probability distributions.
This modern way of "system identification" is more robust than finding
point estimates of a parametric function representation. In this
article, we present a principled algorithm for robust analytic smoothing
in GP dynamic systems, which are increasingly used in robotics and
control. Our numerical evaluations demonstrate the robustness of
the proposed approach in situations where other state-of-the-art
Gaussian filters and smoothers can fail.
Uwe D. Hanebeck, Jannik Steinbring,
Progressive Gaussian Filtering
arXiv preprint: Systems and Control (cs.SY), 2012.
URL
Author : Uwe D. Hanebeck, Jannik Steinbring
Title : Progressive Gaussian Filtering
In : arXiv preprint: Systems and Control (cs.SY)
Address :
Date : 2012
Abstract
In this paper, we propose a progressive Bayesian procedure, where
the measurement information is continuously included into the given
prior estimate (although we perform observations at discrete time
steps). The key idea is to derive a system of ordinary first-order
differential equations (ODE) by employing a new coupled density representation
comprising a Gaussian density and its Dirac Mixture approximation.
The ODE is used for continuously tracking the true non-Gaussian posterior
by its best matching Gaussian approximation. The performance of the
new filter is evaluated in comparison with state-of-the-art filters
by means of a canonical benchmark example, the discrete-time cubic
sensor problem.
Achim Hekler, Jörg Fischer, Uwe D. Hanebeck,
Sequence-Based Control for Networked Control Systems Based on Virtual Control Inputs
arXiv preprint: Systems and Control (cs.SY), 2012.
URL
Author : Achim Hekler, Jörg Fischer, Uwe D. Hanebeck
Title : Sequence-Based Control for Networked Control Systems Based on Virtual Control Inputs
In : arXiv preprint: Systems and Control (cs.SY)
Address :
Date : 2012
Abstract
In this paper, we address the problem of controlling a system over
an unreliable connection that is affected by time-varying delays
and randomly occurring packet losses. A novel sequence-based approach
is proposed that extends a given controller designed without consideration
of the network-induced disturbances. Its key idea is to model the
unknown future control inputs by random variables, the so-called
virtual control inputs, which are characterized by discrete probability
density functions. Subject to this probabilistic description, the
actual sequence of future control inputs is determined and transmitted
to the actuator. The high performance of the proposed approach is
demonstrated by means of Monte Carlo simulation runs with an inverted
pendulum on a cart and by a detailed comparison to standard NCS approaches.
Tobias Kretz, Stefan Hengst, Antonia Pérez Arias, Simon Friedberger, Uwe D. Hanebeck,
Using a Telepresence System to Investigate Route Choice Behavior
arXiv preprint: Human-Computer Interaction (cs.HC), 2011.
URL
Author : Tobias Kretz, Stefan Hengst, Antonia Pérez Arias, Simon Friedberger, Uwe D. Hanebeck
Title : Using a Telepresence System to Investigate Route Choice Behavior
In : arXiv preprint: Human-Computer Interaction (cs.HC)
Address :
Date : 2011
Abstract
A combination of a telepresence system and a microscopic traffic simulator
is introduced. It is evaluated using a hotel evacuation scenario.
Four different kinds of supporting information are compared, standard
exit signs, floor plans with indicated exit routes, guiding lines
on the floor and simulated agents leading the way. The results indicate
that guiding lines are the most efficient way to support an evacuation
but the natural behavior of following others comes very close. On
another level the results are consistent with previously performed
real and virtual experiments and validate the use of a telepresence
system in evacuation studies. It is shown that using a microscopic
traffic simulator extends the possibilities for evaluation, e.g.
by adding simulated humans to the environment.
Daniel Lyons, Jan-P. Calliess, Uwe D. Hanebeck,
Chance-constrained Model Predictive Control for Multi-Agent Systems
arXiv preprint: Systems and Control (cs.SY), 2011.
URL
Author : Daniel Lyons, Jan-P. Calliess, Uwe D. Hanebeck
Title : Chance-constrained Model Predictive Control for Multi-Agent Systems
In : arXiv preprint: Systems and Control (cs.SY)
Address :
Date : 2011
Abstract
We consider stochastic model predictive control of a multi-agent systems
with constraints on the probabilities of inter-agent collisions.
We first study a sample-based approximation of the collision probabilities
and use this approximation to formulate constraints for the stochastic
control problem. This approximation will converge as the number of
samples goes to infinity, however, the complexity of the resulting
control problem is so high that this approach proves unsuitable for
control under real-time requirements. To alleviate the computational
burden we propose a second approach that uses probabilistic bounds
to determine regions with increased probability of presence for each
agent and formulate constraints for the control problem that guarantee
that these regions will not overlap. We prove that the resulting
problem is conservative for the original problem with probabilistic
constraints, ie. every control strategy that is feasible under our
new constraints will automatically be feasible for the original problem.
Furthermore we show in simulations in a UAV path planning scenario
that our proposed approach grants significantly better run-time performance
compared to a controller with the sample-based approximation with
only a small degree of sub-optimality resulting from the conservativeness
of our new approach
Antonia Pérez Arias, Uwe D. Hanebeck, Peter Ehrhardt, Stefan Hengst, Tobias Kretz, Peter Vortisch,
Extended Range Telepresence for Evacuation Training in Pedestrian Simulations
arXiv preprint: Human-Computer Interaction (cs.HC), 2010.
URL
Author : Antonia Pérez Arias, Uwe D. Hanebeck, Peter Ehrhardt, Stefan Hengst, Tobias Kretz, Peter Vortisch
Title : Extended Range Telepresence for Evacuation Training in Pedestrian Simulations
In : arXiv preprint: Human-Computer Interaction (cs.HC)
Address :
Date : 2010
Abstract
In this contribution, we propose a new framework to evaluate pedestrian
simula-tions by using Extended Range Telepresence. Telepresence is
used as a virtual reality walking simulator, which provides the user
with a realistic impression of being present and walking in a virtual
environment that is much larger than the real physical environment,
in which the user actually walks. The validation of the simulation
is performed by comparing motion data of the telepresent user with
simulated data at some points of the simulation. The use of haptic
feedback from the simulation makes the framework suitable for training
in emergency situations.

Publikationen aus dem Jahr 2014

Maxim Dolgov, Jörg Fischer, Uwe D. Hanebeck,
Sequence-based LQG Control with Linear Integral Constraints over Stochastic Networks)
Proceedings of the 53rd IEEE Conference on Decision and Control (CDC 2014), Los Angeles, California, USA, December 2014.
Author : Maxim Dolgov, Jörg Fischer, Uwe D. Hanebeck
Title : Sequence-based LQG Control with Linear Integral Constraints over Stochastic Networks)
In : Proceedings of the 53rd IEEE Conference on Decision and Control (CDC 2014)
Address : Los Angeles, California, USA
Date : December 2014
Abstract
In this paper, we consider sequence-based LQG control
of stochastic linear systems with linear integral state and
input constraints over networks subject to stochastic packet
delays and losses. For this scenario, we derive a
novel closed-loop optimal control law that consists of a
feedback and a feedforward term. The feedback term depends
linearly on the state estimate, while the feedforward term
depends on the initial system state and the constraint functions.
The control law can be partially given in closed-form that
can be precomputed offline, and a numerical part which
demands a solution of a quadratic optimization procedure.
The number of the decision variables corresponds to the
number of constraints. The presented control law is
evaluated by means of a Monte-Carlo simulation.
Gerhard Kurz, Igor Gilitschenski, Uwe D. Hanebeck,
Bivariate Angular Estimation Under Consideration of Dependencies Using Directional Statistics (to appear)
Proceedings of the 53rd IEEE Conference on Decision and Control (CDC 2014), Los Angeles, California, USA, December 2014.
Author : Gerhard Kurz, Igor Gilitschenski, Uwe D. Hanebeck
Title : Bivariate Angular Estimation Under Consideration of Dependencies Using Directional Statistics (to appear)
In : Proceedings of the 53rd IEEE Conference on Decision and Control (CDC 2014)
Address : Los Angeles, California, USA
Date : December 2014
Abstract
Estimation of angular quantities is a widespread
issue, but standard approaches neglect the true topology of the
problem and approximate directional with linear uncertainties.
In recent years, novel approaches based on directional statistics
have been proposed. However, these approaches have been
unable to consider arbitrary circular correlations between
multiple angles so far. For this reason, we propose a novel
recursive filtering scheme that is capable of estimating multiple
angles even if they are dependent, while correctly describing
their circular correlation. The proposed approach is based on
toroidal probability distributions and a circular correlation
coefficient. We demonstrate the superiority to a standard
approach based on the Kalman filter in simulations.
Igor Gilitschenski, Gerhard Kurz, Simon J. Julier, Uwe D. Hanebeck,
Efficient Bingham Filtering based on Saddlepoint Approximations (to appear)
Proceedings of the 2014 IEEE International Conference on Multisensor Fusion and Information Integration(MFI 2014), Beijing, China, September 2014.
Author : Igor Gilitschenski, Gerhard Kurz, Simon J. Julier, Uwe D. Hanebeck
Title : Efficient Bingham Filtering based on Saddlepoint Approximations (to appear)
In : Proceedings of the 2014 IEEE International Conference on Multisensor Fusion and Information Integration(MFI 2014)
Address : Beijing, China
Date : September 2014
Abstract
In this paper, we discuss computational efficiency
of a recursive estimator using the Bingham Distribution. The
Bingham distribution is defined directly on the unit hypersphere.
As such, it is able to describe both large and small uncertainties
in a unified framework. In order to tackle the challenging
computation of the normalization constant, we propose a method
using its Saddlepoint approximations and an approximate MLE
based on the Gauss-Newton method. In a set of simulation
experiments, we demonstrate that the Bingham filter not only
outperforms both Kalman and particle filters, but can also be
implemented efficiently.
Gerhard Kurz, Igor Gilitschenski, Uwe D. Hanebeck,
The Partially Wrapped Normal Distribution for SE(2) Estimation (to appear)
Proceedings of the 2014 IEEE International Conference on Multisensor Fusion and Information Integration(MFI 2014), Beijing, China, September 2014.
Author : Gerhard Kurz, Igor Gilitschenski, Uwe D. Hanebeck
Title : The Partially Wrapped Normal Distribution for SE(2) Estimation (to appear)
In : Proceedings of the 2014 IEEE International Conference on Multisensor Fusion and Information Integration(MFI 2014)
Address : Beijing, China
Date : September 2014
Abstract
We introduce a novel probability distribution on the
group of rigid motions SE(2) and we refer to this distribution
as the partially wrapped normal distribution. Describing probabilities
on the SE(2) is of interest in a wide range of areas, for
example robotics, autonomous vehicles, or information fusion. We
derive some important properties of this novel distribution and
derive an estimation scheme for its parameters based on moment
matching. Furthermore, we provide a comparison to a recently
published approach based on the Bingham distribution, and show
that there are complementary advantages and disadvantages of
the two approaches.
Marc Reinhard, Sanjeev Kulkarni, Uwe D. Hanebeck,
Generalized Covariance Intersection based on Noise Decomposition (to appear)
Proceedings of the 2014 IEEE International Conference on Multisensor Fusion and Information Integration(MFI 2014), Beijing, China, September 2014.
Author : Marc Reinhard, Sanjeev Kulkarni, Uwe D. Hanebeck
Title : Generalized Covariance Intersection based on Noise Decomposition (to appear)
In : Proceedings of the 2014 IEEE International Conference on Multisensor Fusion and Information Integration(MFI 2014)
Address : Beijing, China
Date : September 2014
Abstract
In linear decentralized estimation, several nodes
concurrently aim to recursively estimate the state of a common
phenomenon by means of local measurements and data exchanges
subject to limited knowledge. In this contribution, an efficient
algorithm for consistent estimation in sensor networks under
Kalman filter assumptions is derived. The main theorems generalize
Covariance Intersection by means of an explicit consideration
of individual noise terms. We apply the results to linear
decentralized estimation and obtain covariance bounds with a
scalable precision between the exact covariances and the bounds
provided by Covariance Intersection subject to computation and
communication effort.
Gerhard Kurz, Geneviève Foley, Péter Hegedüs, Gábor Szabó, Uwe D. Hanebeck,
Evaluation of Image Stabilization Methods in Robotic Beating Heart (to appear)
13. Jahrestagung der Deutschen Gesellschaft für Computer- und Roboterassistierte Chirurgie (CURAC14), Munich, Germany, September 2014.
Author : Gerhard Kurz, Geneviève Foley, Péter Hegedüs, Gábor Szabó, Uwe D. Hanebeck
Title : Evaluation of Image Stabilization Methods in Robotic Beating Heart (to appear)
In : 13. Jahrestagung der Deutschen Gesellschaft für Computer- und Roboterassistierte Chirurgie (CURAC14)
Address : Munich, Germany
Date : September 2014
Abstract
Beating heart surgery is a difficult task even for the most skilled
surgeons. To eliminate the risks associated with cardiopulmonary bypass,
a motion compensation system for beating heart surgery can be used. We
place markers on the heart surface in order to track the complex heart
motion and to produce still footage of the heart surface by applying one
of several stabilization algorithms to eliminate the motion. We compare
six different stabilization algorithms, affine, B-spline, piecewise
linear and three types of radial basis functions. In this paper, we
evaluate the results using three evaluation methods, that is, (1) pixel
intensity average difference, (2) optical flow and (3) stabilized marker
tracking, all of which show a significant reduction in motion after
stabilization, especially for interpolation based stabilization methods
as opposed to the affine approximation. We discuss advantages and
disadvantages of the different evaluation methods.
Jörg Fischer, Maxim Dolgov, Uwe D. Hanebeck,
Optimal Sequence-Based Tracking Control over Unreliable Networks (to appear)
Proceedings of the 19th IFAC World Congress (IFAC 2014), Cape Town, South Africa, August 2014.
Author : Jörg Fischer, Maxim Dolgov, Uwe D. Hanebeck
Title : Optimal Sequence-Based Tracking Control over Unreliable Networks (to appear)
In : Proceedings of the 19th IFAC World Congress (IFAC 2014)
Address : Cape Town, South Africa
Date : August 2014
Uwe D. Hanebeck, Anders Lindquist,
Moment-based Dirac Mixture Approximation of Circular Densities (to appear)
Proceedings of the 19th IFAC World Congress (IFAC 2014), Cape Town, South Africa, August 2014.
Author : Uwe D. Hanebeck, Anders Lindquist
Title : Moment-based Dirac Mixture Approximation of Circular Densities (to appear)
In : Proceedings of the 19th IFAC World Congress (IFAC 2014)
Address : Cape Town, South Africa
Date : August 2014
Benjamin Noack, Joris Sijs, Uwe D. Hanebeck,
Fusion Strategies for Unequal State Vectors in Distributed Kalman Filtering (to appear)
Proceedings of the 19th IFAC World Congress (IFAC 2014), Cape Town, South Africa, August 2014.
Author : Benjamin Noack, Joris Sijs, Uwe D. Hanebeck
Title : Fusion Strategies for Unequal State Vectors in Distributed Kalman Filtering (to appear)
In : Proceedings of the 19th IFAC World Congress (IFAC 2014)
Address : Cape Town, South Africa
Date : August 2014
Jiří Ajgl, Miroslav Šimandl, Marc Reinhardt, Benjamin Noack, Uwe D. Hanebeck,
Covariance Intersection in State Estimation of Dynamical Systems
Proceedings of the 17th International Conference on Information Fusion (Fusion 2014), Salamanca, Spain, July 2014.
PDF
Author : Jiří Ajgl, Miroslav Šimandl, Marc Reinhardt, Benjamin Noack, Uwe D. Hanebeck
Title : Covariance Intersection in State Estimation of Dynamical Systems
In : Proceedings of the 17th International Conference on Information Fusion (Fusion 2014)
Address : Salamanca, Spain
Date : July 2014
Abstract
The Covariance Intersection algorithm linearly
combines estimates when the cross-correlations between their
errors are unknown. It provides a fused estimate and an upper
bound of the corresponding mean square error matrix. The
weights of the linear combination are designed in order to
minimise the upper bound. This paper analyses the optimal
weights in relation to state estimation of dynamical systems.
It is shown that the use of the optimal upper bound in a
standard recursive filtering does not lead to optimal upper bounds
in subsequent processing steps. Unlike the fusion under full
knowledge, the fusion under unknown cross-correlations can fuse
the same information differently, depending on the independent
information that will be available in the future.
Zhansheng Duan, Xiao-Rong Li, Uwe D. Hanebeck,
Multi-sensor Distributed Estimation Fusion Using Minimum Distance Sum
Proceedings of the 17th International Conference on Information Fusion (Fusion 2014), Salamanca, Spain, July 2014.
PDF
Author : Zhansheng Duan, Xiao-Rong Li, Uwe D. Hanebeck
Title : Multi-sensor Distributed Estimation Fusion Using Minimum Distance Sum
In : Proceedings of the 17th International Conference on Information Fusion (Fusion 2014)
Address : Salamanca, Spain
Date : July 2014
Abstract
In multi-sensor distributed estimation fusion, local
estimation errors are correlated in general. Two extreme ways to
handle this correlation is either to ignore them completely or to
have them fully considered. There is another case in the middle:
it admits the existence of the correlation, but does not know how
large it is. A sensible way is to set up an optimality criterion
and optimize it over all possible such correlations. This work
is a new development in the third class. First, a new general
objective function is introduced, which is the minimum sum
of statistical distances between the fused density and the local
posterior densities. Then it is shown that the new criterion leads
to a convex optimization problem if the Kullback-Leibler (KL)
divergence is used as the statistical distance between assumed
Gaussian densities. It is found that although the analytically
obtained fused estimate using the new criterion differs from
the simple convex combination rule only in mean squared error
(MSE) by a scaling factor N (the number of sensors used), it is
pessimistic semi-definite in MSE. Numerical examples illustrate
the effectiveness of the proposed distributed fuser by comparing
with several widely used distributed fusers.
Christof Chlebek, Uwe D. Hanebeck,
Pole-based Distance Measure for Change Detection in Linear Dynamic Systems
Proceedings of the 17th International Conference on Information Fusion (Fusion 2014), Salamanca, Spain, July 2014.
PDF
Author : Christof Chlebek, Uwe D. Hanebeck
Title : Pole-based Distance Measure for Change Detection in Linear Dynamic Systems
In : Proceedings of the 17th International Conference on Information Fusion (Fusion 2014)
Address : Salamanca, Spain
Date : July 2014
Abstract
In this work, we derive a distance measure for
the detection of changes in the behavior of linear dynamic
single-input-single-output (SISO) systems based on input-output
data. The distance is calculated as a function of the system
poles, which are directly estimated from the given data. Poles
represent a system as a set and have no identities, which is
analogous to the nature of association-free multi-target tracking.
This motivates the application of set distances known from
multi-target tracking, namely the optimal subpattern assign-
ment (OSPA) distance. Thus, the OSPA distance as well as a
modification, the MAX-OSPA distance, are formulated as pole-
distances between dynamic systems. In this formulation, the
OSPA distance finds the optimal assingment by minimizing over
the sum of distances between poles. The MAX-OSPA chooses
an optimal assignment by minimizing the maximum distance
between two poles. The proposed distances are evaluated in
several simulations comparing the deterministic OSPA and MAX-
OSPA to a state-of-the-art metric for autoregressive-moving-
average (ARMA) processes, as well as OSPA and MAX-OSPA
using the direct pole estimation and a two step-pole estimation
utilizing recursive ARX (AutoRegressive model with eXogenous
input) system identification.
Florian Faion, Antonio Zea, Uwe D. Hanebeck,
Reducing Bias in Bayesian Shape Estimation
Proceedings of the 17th International Conference on Information Fusion (Fusion 2014), Salamanca, Spain, July 2014.
PDF
Author : Florian Faion, Antonio Zea, Uwe D. Hanebeck
Title : Reducing Bias in Bayesian Shape Estimation
In : Proceedings of the 17th International Conference on Information Fusion (Fusion 2014)
Address : Salamanca, Spain
Date : July 2014
Abstract
This work considers the problem of estimating the
parameters of an extended object based on noisy point observations
from its boundary. The intention is to explore relationships
between common approaches by breaking them down into their
basic assumptions within the Bayesian framework. In doing so,
we find that distance-minimizing curve fitting algorithms can be
modeled by using a special Spatial Distribution Model, where
the source distribution is approximated by a greedy one-to-one
association of points to sources on the shape boundary. Based on
this insight, we explore the origin of the estimation bias, which is
a well-known issue of curve fitting algorithms. Furthermore, we
derive a general scheme to alleviate its effect for arbitrary shapes,
as well as for non-isotropic noise. This procedure is shown to be
a generalization of related special solutions.
Igor Gilitschenski, Gerhard Kurz, Simon J. Julier, Uwe D. Hanebeck,
A New Probability Distribution for Simultaneous Representation of Uncertain Position and Orientation
Proceedings of the 17th International Conference on Information Fusion (Fusion 2014), Salamanca, Spain, July 2014.
PDF
Author : Igor Gilitschenski, Gerhard Kurz, Simon J. Julier, Uwe D. Hanebeck
Title : A New Probability Distribution for Simultaneous Representation of Uncertain Position and Orientation
In : Proceedings of the 17th International Conference on Information Fusion (Fusion 2014)
Address : Salamanca, Spain
Date : July 2014
Abstract
This work proposes a novel way to represent uncertainty
on the Lie group of rigid-body motions in the plane. This is
achieved by using dual quaternions for representation of a planar
rigid-body motion and proposing a probability distribution from
the exponential family of distributions that inherently respects the
underlying structure of the representation. This is particularly
beneficial in scenarios involving strong measurement noise. A
relationship between the newly proposed distributional model
and the Bingham distribution is discussed. The presented results
involve formulas for computation of the normalization constant,
the mode, parameter estimation techniques, and a closed-form
Bayesian measurement fusion.
Best Student Paper Award First Runner-Up
Igor Gilitschenski, Jannik Steinbring, Uwe D. Hanebeck, Miroslav Simandl,
Deterministic Dirac Mixture Approximation of Gaussian Mixtures
Proceedings of the 17th International Conference on Information Fusion (Fusion 2014), Salamanca, Spain, July 2014.
PDF
Author : Igor Gilitschenski, Jannik Steinbring, Uwe D. Hanebeck, Miroslav Simandl
Title : Deterministic Dirac Mixture Approximation of Gaussian Mixtures
In : Proceedings of the 17th International Conference on Information Fusion (Fusion 2014)
Address : Salamanca, Spain
Date : July 2014
Abstract
In this work, we propose a novel way to approximating
mixtures of Gaussian distributions by a set of deterministically
chosen Dirac delta components. This approximation is performed
by adapting a method for approximating single Gaussian
distributions to the considered case. The proposed method turns
the approximation problem into an optimization problem by
minimizing a distance measure between the Gaussian mixture
and its Dirac mixture approximation. Compared to the simple
Gaussian case, the minimization criterion is much more complex
as multiple, non-standard Gaussian distributions have to be considered.
Uwe D. Hanebeck,
Sample Set Design for Nonlinear Kalman Filters viewed as a Moment Problem
Proceedings of the 17th International Conference on Information Fusion (Fusion 2014), Salamanca, Spain, July 2014.
PDF
Author : Uwe D. Hanebeck
Title : Sample Set Design for Nonlinear Kalman Filters viewed as a Moment Problem
In : Proceedings of the 17th International Conference on Information Fusion (Fusion 2014)
Address : Salamanca, Spain
Date : July 2014
Abstract
For designing sample sets for nonlinear Kalman
filters, i.e., Linear Regression Kalman Filters (LRKFs), a new
method is introduced for approximating Gaussian densities by
discrete densities, so called Dirac Mixtures (DMs). This
approximating DM should maintain the mean and some higher-
order moments and should homogeneously cover the support
of the original density. Homogeneous approximations require
redundancy, which means there are more Dirac components than
necessary for fulfilling the moment constraints. Hence, some
sort of regularization is required as the solution is no longer
unique. Two types of regularizers are possible: The first type
ensures smooth approximations, e.g., in a maximum entropy
sense. The second type we pursue here ensures closeness of
the approximating density to the given Gaussian. As standard
distance measures are typically not well defined for discrete
densities on continuous domains, we focus on shifting the mass
distribution of the approximating density as close to the true
density as possible. Instead of globally comparing the masses as
in a previous paper, the key idea is to characterize individual
Dirac components by kernel functions representing the spread
of probability mass that is appropriate at a given location. A
distance measure is then obtained by comparing the deviation
between the true density and the induced kernel density. As a
result, the approximation problem is converted to an optimization
problem as we now minimize the distance under the desired
moment constraints.
Gerhard Kurz, Igor Gilitschenski, Uwe D. Hanebeck,
Deterministic Approximation of Circular Densities with Symmetric Dirac Mixtures Based on Two Circular Moments
Proceedings of the 17th International Conference on Information Fusion (Fusion 2014), Salamanca, Spain, July 2014.
PDF
Author : Gerhard Kurz, Igor Gilitschenski, Uwe D. Hanebeck
Title : Deterministic Approximation of Circular Densities with Symmetric Dirac Mixtures Based on Two Circular Moments
In : Proceedings of the 17th International Conference on Information Fusion (Fusion 2014)
Address : Salamanca, Spain
Date : July 2014
Abstract
Circular estimation problems arise in many applications and can be
addressed with the help of circular distributions. In particular, the
wrapped normal and von Mises distributions are widely used in the
context of circular problems. To facilitate the development of nonlinear
filters, a deterministic sample-based approximation of these
distributions with a so-called wrapped Dirac mixture distribution is
beneficial. We propose a new closed-form solution to obtain a symmetric
wrapped Dirac mixture with five components based on matching the first
two circular moments. The proposed method is superior to
state-of-the-art methods, which only use the first circular moment to
obtain three Dirac components, because a larger number of Dirac
components results in a more accurate approximation.
Winner Best Paper Award
Gerhard Kurz, Uwe D. Hanebeck,
2D and 3D Image Stabilization for Robotic Beating Heart Surgery
Proceedings of the 17th International Conference on Information Fusion (Fusion 2014), Salamanca, Spain, July 2014.
PDF
Author : Gerhard Kurz, Uwe D. Hanebeck
Title : 2D and 3D Image Stabilization for Robotic Beating Heart Surgery
In : Proceedings of the 17th International Conference on Information Fusion (Fusion 2014)
Address : Salamanca, Spain
Date : July 2014
Abstract
Image stabilization is relevant for various industrial and medical
applications. In particular, we consider the use of image stabilization
in robotic beating heart surgery. A robot, which is remotely controlled
by the surgeon, can automatically compensate for the motion of the
beating heart. To give the surgeon the illusion of operating on a
stationary heart, a stabilized image of the beating heart is shown to
the surgeon. Image stabilization cancels the unwanted motion of the
heart, but retains changes to color and texture, for example cuts on the
heart surface. In this paper, stabilization is first considered as a 2D
image transformation problem. Subsequently, it is extended to
stabilization of a 3D point cloud or surface. The proposed algorithms
are evaluated in both ex-vivo and in-vivo experiments. In the
evaluation, the stabilization quality achievable with several common
interpolation functions is compared.
Benjamin Noack, Marc Reinhardt, Uwe D. Hanebeck,
On Nonlinear Track-to-track Fusion with Gaussian Mixtures
Proceedings of the 17th International Conference on Information Fusion (Fusion 2014), Salamanca, Spain, July 2014.
PDF
Author : Benjamin Noack, Marc Reinhardt, Uwe D. Hanebeck
Title : On Nonlinear Track-to-track Fusion with Gaussian Mixtures
In : Proceedings of the 17th International Conference on Information Fusion (Fusion 2014)
Address : Salamanca, Spain
Date : July 2014
Abstract
The problem of fusing state estimates is encountered in many network-based multi-sensor applications.
The majority of distributed state estimation algorithms are designed to provide multiple estimates on the
same state, and track-to-track fusion then refers to the task of combining these estimates. While linear
fusion only requires the joint cross-covariance matrix to be known, dependencies between estimates in
nonlinear estimation problems have to be represented by high-dimensional probability density functions.
In general, storing and keeping track of nonlinear dependencies is too cumbersome. However, this paper
demonstrates that estimates represented by Gaussian mixtures prove to be an important exception to this rule.
The dependency structure can as well be characterized in terms of a higher-dimensional Gaussian mixture.
The different processing steps of distributed nonlinear state estimation, i.e., prediction, filtering,
and fusion, are studied in light of the joint density representation. The presented concept is complemented
with different simpler suboptimal representations of the dependency structure between Gaussian mixture densities.
Marc Reinhardt, Benjamin Noack, Sanjeev Kulkarni, Uwe D. Hanebeck,
Distributed Kalman Filtering in the Presence of Packet Delays and Losses
Proceedings of the 17th International Conference on Information Fusion (Fusion 2014), Salamanca, Spain, July 2014.
PDF
Author : Marc Reinhardt, Benjamin Noack, Sanjeev Kulkarni, Uwe D. Hanebeck
Title : Distributed Kalman Filtering in the Presence of Packet Delays and Losses
In : Proceedings of the 17th International Conference on Information Fusion (Fusion 2014)
Address : Salamanca, Spain
Date : July 2014
Abstract
Distributed Kalman filtering aims at optimizing an
estimate at a fusion center based on information that is gathered
in a sensor network. Recently, an exact solution based on local
estimation tracks has been proposed and an extension to cope
with packet losses has been derived. In this contribution, we
generalize both algorithms to packet delays. The key idea is
to introduce augmented measurement vectors in the sensors
that permit the optimization of local filter gains according to
time-dependent measurement capabilities at the fusion center.
In the most general form, the algorithm provides optimized
estimates in sensor networks with packets delays and losses. The
precision depends on the actual arrival patterns, and the results
correspond to those of the centralized Kalman filter when specific
assumptions about the measurement capability are satisfied.
Jannik Steinbring, Uwe D. Hanebeck,
Progressive Gaussian Filtering Using Explicit Likelihoods
Proceedings of the 17th International Conference on Information Fusion (Fusion 2014), Salamanca, Spain, July 2014.
PDF
Author : Jannik Steinbring, Uwe D. Hanebeck
Title : Progressive Gaussian Filtering Using Explicit Likelihoods
In : Proceedings of the 17th International Conference on Information Fusion (Fusion 2014)
Address : Salamanca, Spain
Date : July 2014
Abstract
In this paper, we introduce a new sample-based
Gaussian filter. In contrast to the popular Nonlinear Kalman
Filters, e.g., the UKF, we do not rely on linearizing the
measurement model. Instead, we take up the Gaussian progressive
filtering approach introduced by the PGF 42 but explicitly rely
on likelihood functions. Progression means, we incorporate the
information of a new measurement gradually into the state
estimate. The advantages of this filtering method are on the one
hand the avoidance of sample degeneration and on the other
hand an adaptive determination of the number of likelihood
evaluations required for each measurement update. By this
means, less informative measurements can be processed quickly,
whereas measurements containing much information automatically
receive more emphasis by the filter. These properties allow
the new filter to cope with the demanding problem of very narrow
likelihood functions in an efficient way.
Antonio Zea, Florian Faion, Uwe D. Hanebeck,
Tracking Connected Objects Using Interacting Shape Models
Proceedings of the 17th International Conference on Information Fusion (Fusion 2014), Salamanca, Spain, July 2014.
PDF
Author : Antonio Zea, Florian Faion, Uwe D. Hanebeck
Title : Tracking Connected Objects Using Interacting Shape Models
In : Proceedings of the 17th International Conference on Information Fusion (Fusion 2014)
Address : Salamanca, Spain
Date : July 2014
Abstract
As sensor resolution increases, estimators tracking
extended objects benefit from being able to closely model the
shape of the target. However, as more shape details are
incorporated, this usually leads to increasingly complex estimators.
A more useful approach is to describe these shapes as a combination
of simpler shapes connected to each other. In this paper, we
propose a modular approach to estimate these combined targets
in function of their simpler components. This allows the
characteristics of each component to be encapsulated, and permits
the combination of multiple filtering techniques as required by
each component shape. This approach can be applied to track
combined objects in a large variety of environments, such as
excavators, robotic arms, wagon trains, and many others.
Christof Chlebek, Uwe D. Hanebeck,
Bayesian Approach to Direct Pole Estimation
Proceedings of the 2014 European Control Conference (ECC 2014), Strasbourg, France, June 2014.
PDF
Author : Christof Chlebek, Uwe D. Hanebeck
Title : Bayesian Approach to Direct Pole Estimation
In : Proceedings of the 2014 European Control Conference (ECC 2014)
Address : Strasbourg, France
Date : June 2014
Abstract
In this work, a solution to the direct pole identification problem
of discrete-time autoregressive (AR) processes by general recursive
Bayesian estimation is presented. The poles of the transfer function
of an AR process are identified directly from the process output
data. Without intermediate estimation of the AR coefficient, the
AR process identification problem by means of its poles becomes nonlinear,
and thus cannot be solved exactly. A practical solution by application
of statistical linearization is given. The derived direct pole estimation
algorithm by statistical linearization is given in closed-form and
regression point based, by the so-called Linear Regression Kalman
Filter (LRKF). Two realizations of the LRKF algorithm are tested,
namely the Unscented Kalman Filter (UKF) for low computational complexity
and thus, for high update rates, and the Smart Sampling Kalman Filter
(S2KF) for high precision with faster convergence. Both, the UKF
and S2KF are compared to the Adaptive Pole Estimation (APE), a solution
by recursive nonlinear least squares minimizing the prediction error
gradient.
Marcus Baum, Peter Willett, Uwe D. Hanebeck,
MMOSPA-based Direction-of-Arrival Estimation for Planar Antenna Arrays
Proceedings of the Eighth IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM 2014), A Coruña, Spain, June 2014.
PDF
Author : Marcus Baum, Peter Willett, Uwe D. Hanebeck
Title : MMOSPA-based Direction-of-Arrival Estimation for Planar Antenna Arrays
In : Proceedings of the Eighth IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM 2014)
Address : A Coruña, Spain
Date : June 2014
Abstract
This work is concerned with direction-of-arrival
(DOA) estimation of narrowband signals from multiple targets
using a planar antenna array. We illustrate the shortcomings of
Maximum Likelihood (ML), Maximum a Posteriori (MAP), and
Minimum Mean Squared Error (MMSE) estimation, issues that
can be attributed to the symmetry in the likelihood function that
must exist when there is no information about labeling of targets.
We proffer the recently introduced concept of Minimum Mean
OSPA (MMOSPA) estimation that is based on the optimal subpattern
assignment (OSPA) metric for sets and hence inherently
incorporates symmetric likelihood functions.
Gerhard Kurz, Marcus Baum, Uwe D. Hanebeck,
Real-time Kernel-based Multiple Target Tracking for Robotic Beating Heart Surgery
Proceedings of the Eighth IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM 2014), A Coruña, Spain, June 2014.
PDF
Author : Gerhard Kurz, Marcus Baum, Uwe D. Hanebeck
Title : Real-time Kernel-based Multiple Target Tracking for Robotic Beating Heart Surgery
In : Proceedings of the Eighth IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM 2014)
Address : A Coruña, Spain
Date : June 2014
Abstract
Performing surgery on the beating heart has significant advantages for
the patient compared to traditional heart surgery on the stopped heart.
A remote-controlled robot can be used to automatically cancel out the
movement of the beating heart. This necessitates precise tracking of the
heart surface. For this purpose, we track 24 identical artificial
markers placed on the heart. This creates a data association problem,
because it is not known which measurement was obtained from which
marker. To solve this problem, we apply a multiple target tracking
method based on a symmetric kernel transformation. This method allows
efficient handling of the data association problem even for a reasonably
large number of targets. We demonstrate how to implement this method
efficiently. The proposed approach is evaluated on in-vivo data of a
real beating heart surgery performed on a porcine beating heart.
Antonio Zea, Florian Faion, Marcus Baum, Uwe D. Hanebeck,
Tracking Simplified Shapes Using a Stochastic Boundary
Proceedings of the Eighth IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM 2014), A Coruña, Spain, June 2014.
PDF
Author : Antonio Zea, Florian Faion, Marcus Baum, Uwe D. Hanebeck
Title : Tracking Simplified Shapes Using a Stochastic Boundary
In : Proceedings of the Eighth IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM 2014)
Address : A Coruña, Spain
Date : June 2014
Abstract
When tracking extended objects, it is often the case that the shape of the
target cannot be fully observed due to issues of visibility, artifacts, or
high noise, which can change with time.
In these situations, it is a common approach to model targets as simpler
shapes instead, such as ellipsoids or cylinders.
However, these simplifications cause information loss from the original
shape, which could be used to improve the estimation results.
In this paper, we propose a way to recover information from these lost
details in the form of a stochastic boundary, whose parameters can be
dynamically estimated from received measurements.
The benefits of this approach are evaluated by tracking an object using
noisy, real-life RGBD data.
Maxim Dolgov, Gerhard Kurz, Uwe D. Hanebeck,
State Estimation for Stochastic Hybrid Systems Based on Deterministic Dirac Mixture Approximation
Proceedings of the 2014 American Control Conference (ACC 2014), Portland, Oregon, USA, June 2014.
PDF
Author : Maxim Dolgov, Gerhard Kurz, Uwe D. Hanebeck
Title : State Estimation for Stochastic Hybrid Systems Based on Deterministic Dirac Mixture Approximation
In : Proceedings of the 2014 American Control Conference (ACC 2014)
Address : Portland, Oregon, USA
Date : June 2014
Abstract
In this paper, we consider state estimation for Stochastic Hybrid
Systems (SHS). These are systems that possess both continuous-valued and
discrete-valued dynamics. For SHS with nonlinear hybrid dynamics and/or
non-Gaussian disturbances, state estimation can be implemented as an
Interacting Multiple Model (IMM) particle filter. However, a
disadvantage of particle filtering is the computational load caused by
the large number of particles required for a sufficiently good
estimation. We address this issue by first expressing the probability
density that describes the state of the SHS as a collection of densities
of the continuous-valued state only conditioned on the discrete-valued
state. Then, we deterministically approximate these individual densities
with Dirac mixtures. The employed approximation method places the
particles so that a so called modified Cram\\\\ér-von Mises distance
between the true and the approximated density is minimized.
Deterministic approximation requires far less particles than the
stochastic sampling used by particle filters. To avoid particle
degeneration that can occur when a density is multiplied with the
likelihood, the filter uses progressive density correction. The
presented filter is demonstrated in a numerical maneuvering target
tracking example.
Gerhard Kurz, Igor Gilitschenski, Uwe D. Hanebeck,
Nonlinear Measurement Update for Estimation of Angular Systems Based on Circular Distributions
Proceedings of the 2014 American Control Conference (ACC 2014), Portland, Oregon, USA, June 2014.
PDF
Author : Gerhard Kurz, Igor Gilitschenski, Uwe D. Hanebeck
Title : Nonlinear Measurement Update for Estimation of Angular Systems Based on Circular Distributions
In : Proceedings of the 2014 American Control Conference (ACC 2014)
Address : Portland, Oregon, USA
Date : June 2014
Abstract
In this paper, we propose a novel progressive nonlinear measurement
update for circular states. This generalizes our previously published
circular filter that so far was limited to identity measurement
equations. The new update method is based on circular distributions in
order to capture the periodic properties of a circular system better
than conventional approaches that rely on standard Gaussian
distributions. Besides the progressive measurement update, we propose
two additional measurement updates that are obtained by adapting
traditional filters to the circular case. Simulations show the
superiority of the proposed progressive approach.
Gerhard Kurz, Uwe D. Hanebeck,
Dynamic Surface Reconstruction by Recursive Fusion of Depth and Position Measurements
Journal of Advances in Information Fusion, 9(1):13 - 26, June 2014.
PDF
Author : Gerhard Kurz, Uwe D. Hanebeck
Title : Dynamic Surface Reconstruction by Recursive Fusion of Depth and Position Measurements
In : Journal of Advances in Information Fusion
Address :
Date : June 2014
Abstract
Surface estimation can be performed based on position or depth measurements.
We propose a method to fuse both types of measurements. Position measurements are obtained
from landmarks on the surface, i.e., they are fixed to a certain point on the surface. In
contrast, depth measurements reflect the depth measured along a line emanating from a depth
camera and are not fixed to a position on the surface. The proposed approach uses a mixture
of Cartesian and polar or spherical coordinate to treat both measurement types accordingly.
By doing so, the uncertainties associated with the different measurement types are explicitly
considered. The presented method represents the surface by a spline and is applicable to both
2D and 3D applications. Surface estimation is considered as a recursive filtering problem and
standard nonlinear filtering methods suchas the unscented Kalman filter can be used to obtain
surface estimates. We show a thorough evaluation of the proposed approach in simulations.
Marc Reinhardt, Benjamin Noack, Uwe D. Hanebeck,
Reconstruction of Joint Covariance Matrices in Networked Linear Systems
Proceedings of the 48th Annual Conference on Information Sciences and Systems (CISS 2014), Princeton, New Jersey, USA, March 2014.
PDF
Author : Marc Reinhardt, Benjamin Noack, Uwe D. Hanebeck
Title : Reconstruction of Joint Covariance Matrices in Networked Linear Systems
In : Proceedings of the 48th Annual Conference on Information Sciences and Systems (CISS 2014)
Address : Princeton, New Jersey, USA
Date : March 2014
Abstract
In this paper, a sample representation of the estimation error is
utilized to reconstruct the joint covariance matrix in a distributed estimation
system. The key idea is to sample uncorrelated and fully correlated noise
according to different techniques at local estimators without knowledge about
the processing of other nodes in the network. This way, the correlation between
estimates is inherently linked to the representation of the corresponding
sample sets. We discuss the noise processing, derive key attributes, and
evaluate the precision of the covariance estimates.
Uwe D. Hanebeck,
Kernel-based Deterministic Blue-noise Sampling of Arbitrary Probability Density Functions
Proceedings of the 48th Annual Conference on Information Sciences and Systems (CISS 2014), Princeton, New Jersey, USA, March 2014.
PDF
Author : Uwe D. Hanebeck
Title : Kernel-based Deterministic Blue-noise Sampling of Arbitrary Probability Density Functions
In : Proceedings of the 48th Annual Conference on Information Sciences and Systems (CISS 2014)
Address : Princeton, New Jersey, USA
Date : March 2014
Abstract
This paper provides an efficient method for approximating a given
continuous probability density function (pdf) by a Dirac mixture density. Optimal
parameters are determined by systematically minimizing a distance measure. As
standard distance measures are typically not well defined for discrete densities
on continuous domains, we focus on shifting the mass distribution of the
approximating density as close to the true density as possible. Instead of globally
comparing the masses as in a previous paper, the key idea is to characterize
individual Dirac components by kernel functions representing the spread of
probability mass that is appropriate at a given location. A distance measure is
then obtained by comparing the deviation between the true density and the induced
kernel density. This new method for Dirac mixture approximation provides
high-quality approximation results, can handle arbitrary pdfs, allows considering
constraints for, e.g., maintaining certain moments, and is fast enough for online
processing.
Jörg Fischer, Uwe D. Hanebeck,
Distributed and Networked Model Predictive Control
Control Theory of Digitally Networked Dynamic Systems, pp. 111-167, Springer International Publishing, 2014.
PDF URL
Author : Jörg Fischer, Uwe D. Hanebeck
Title : Distributed and Networked Model Predictive Control
In : Control Theory of Digitally Networked Dynamic Systems
Address :
Date : 2014
Abstract
In this chapter, we consider the problem of controlling networked
and distributed systems by means of model predictive control (MPC).
The basic idea behind MPC is to repeatedly solve an optimal control
problem based on a model of the system to be controlled. Every time
a new measurement is available, the optimization problem is solved
and the corresponding input sequence is applied until a new measurement
arrives. As explained in the sequel, the advantages of MPC over other
control strategies for networked systems are due to the fact that
a model of the system is available at the controller side, which
can be used to compensate for random bounded delays. At the same
time, for each iteration of the optimization problem an optimal input
sequence is calculated. In case of packet dropouts, one can reuse
this information to maintain closed-loop stability and performance.

Publikationen aus dem Jahr 2013

Jörg Fischer, Maxim Dolgov, Uwe D. Hanebeck,
On Stability of Sequence-Based LQG Control
Proceedings of the 52st IEEE Conference on Decision and Control (CDC 2013), Florence, Italy, December 2013.
PDF
Author : Jörg Fischer, Maxim Dolgov, Uwe D. Hanebeck
Title : On Stability of Sequence-Based LQG Control
In : Proceedings of the 52st IEEE Conference on Decision and Control (CDC 2013)
Address : Florence, Italy
Date : December 2013
Abstract
Sequence-based control is a well-established method applied in
Networked Control Systems (NCS) to mitigate the effect of time-varying
transmission delays and stochastic packet losses. The idea of this method
is that the controller sends sequences of predicted control inputs to the
actuator that can be applied in case a future transmission fails. In this paper,
the stability properties of sequence-based LQG controllers are analyzed
in terms of the boundedness of the long run average costs. On the one hand,
we derive sufficient conditions, each for the boundedness and unboundedness
of the costs. On the other hand, we give bounds on the minimal length of
the control input sequence needed to stabilize a system.
Gerhard Kurz, Peter Hegedus, Gabor Szabo, Uwe D. Hanebeck,
Experimental Evaluation of Kinect and Inertial Sensors for Beating Heart Tracking
12. Jahrestagung der Deutschen Gesellschaft für Computer- und Roboterassistierte Chirurgie (CURAC13), Innsbruck, Austria, November 2013.
PDF
Author : Gerhard Kurz, Peter Hegedus, Gabor Szabo, Uwe D. Hanebeck
Title : Experimental Evaluation of Kinect and Inertial Sensors for Beating Heart Tracking
In : 12. Jahrestagung der Deutschen Gesellschaft für Computer- und Roboterassistierte Chirurgie (CURAC13)
Address : Innsbruck, Austria
Date : November 2013
Abstract
This paper investigates the use of Kinect depth sensors as well as
inertial sensors in the context of beating heart surgery. In the past,
various sensors have been used in attempts to track the beating heart,
each with its own distinct set of advantages and disadvantages. With the
availability of affordable structured-light depth sensors such as the
Kinect and sufficiently small and low priced inertial sensors, the
question of their suitability for beating heart tracking arises. We
performed in-vivo experiments on a porcine heart in order to assess the
feasibility of beating heart tracking based on these sensors.
Gerhard Kurz, Florian Faion, Uwe D. Hanebeck,
Constrained Object Tracking on Compact One-dimensional Manifolds Based on Directional Statistics
Proceedings of the Fourth IEEE GRSS International Conference on Indoor Positioning and Indoor Navigation (IPIN 2013), Montbeliard, France, October 2013.
PDF
Author : Gerhard Kurz, Florian Faion, Uwe D. Hanebeck
Title : Constrained Object Tracking on Compact One-dimensional Manifolds Based on Directional Statistics
In : Proceedings of the Fourth IEEE GRSS International Conference on Indoor Positioning and Indoor Navigation (IPIN 2013)
Address : Montbeliard, France
Date : October 2013
Abstract
In this paper, we present a novel approach for tracking objects whose
movement is constrained to a compact one-dimensional manifold, for
example a conveyer belt or a mobile robot whose movement is restricted
to tracks. Standard approaches either ignore the constraint at first and
retroactively move the estimate to lie on the manifold, or consider the
tracking problem on a manifold but falsely assume a Gaussian
distribution. Our method explicitly takes the actual topology into
account from the beginning and relies on special types of probability
distributions defined on the proper manifold. In particular, we consider
objects moving along a closed one-dimensional track, for example an
ellipse, a polygon, or similar closed shapes. This shape is transformed
to a circle with a homeomorphism. Thus, we can apply a recursive
circular filtering algorithm to the constrained tracking problem.
Finally, the estimate is transformed back to the original manifold. We
evaluate the proposed method in an experiment by tracking a toy train
moving along a track and comparing the results to those of traditional
approaches for this problem.
Maxim Dolgov, Jörg Fischer, Uwe D. Hanebeck,
Event-based LQG Control over Networks Subject to Random Transmission Delays and Packet Losses
Proceedings of the 4th IFAC Workshop on Distributed Estimation and Control in Networked Systems (NecSys 2013), Koblenz, Germany, September 2013.
PDF
Author : Maxim Dolgov, Jörg Fischer, Uwe D. Hanebeck
Title : Event-based LQG Control over Networks Subject to Random Transmission Delays and Packet Losses
In : Proceedings of the 4th IFAC Workshop on Distributed Estimation and Control in Networked Systems (NecSys 2013)
Address : Koblenz, Germany
Date : September 2013
Abstract
In Networked Control Systems (NCS), data networks not only
limit the amount of information exchanged by system components but
are also subject to stochastic packet delays and losses. In this
paper, we present a controller that simultaneously addresses these
problems by combining event-based and sequence-based control methods.
At every time step, the proposed controller calculates a sequence
of predicted control inputs and based on the expected future LQG
costs decides whether it transmits the control sequence to the actuator.
The proposed controller is evaluated with simulations.
Joris Sijs, Uwe D. Hanebeck, Benjamin Noack,
An Empirical Method to Fuse Partially Overlapping State Vectors for Distributed State Estimation
Proceedings of the 2013 European Control Conference (ECC 2013), Zürich, Switzerland, July 2013.
PDF
Author : Joris Sijs, Uwe D. Hanebeck, Benjamin Noack
Title : An Empirical Method to Fuse Partially Overlapping State Vectors for Distributed State Estimation
In : Proceedings of the 2013 European Control Conference (ECC 2013)
Address : Zürich, Switzerland
Date : July 2013
Abstract
State fusion is a method for merging multiple estimates of the same
state into a single fused estimate. Dealing with multiple estimates
is one of the main concerns in distributed state estimation, where
an estimated value of the desired state vector is computed in each
node of a networked system. Most solutions for distributed state
estimation currently available assume that every node computes an
estimate of the (same) global state vector. This assumption is impractical
for systems observing large-area processes, due to the sheer size
of the process state. A more feasible solutions is one where each
node estimates a part of the global state vector, allowing different
nodes in the network to have overlapping state elements. Although
such an approach should be accompanied by a corresponding state fusion
method, existing solutions cannot be employed as they merely consider
fusion of two different estimates with equal state representations.
Therefore, an empirical solution is presented for fusing two state
estimates that have partially overlapping state elements. A justification
of the proposed fusion method is presented, along with an illustrative
case study for observing the temperature profile of a large rod,
though a formal derivation is future research.
Marcus Baum, Uwe D. Hanebeck,
The Kernel-SME Filter for Multiple Target Tracking
Proceedings of the 16th International Conference on Information Fusion (Fusion 2013), Istanbul, Turkey, July 2013.
PDF
Author : Marcus Baum, Uwe D. Hanebeck
Title : The Kernel-SME Filter for Multiple Target Tracking
In : Proceedings of the 16th International Conference on Information Fusion (Fusion 2013)
Address : Istanbul, Turkey
Date : July 2013
Abstract
We present a novel method for tracking multiple targets, called Kernel-SME
filter, that does not require an enumeration of measurement-to-target
associations. This method is a further development of the symmetric
measurement equation (SME) filter that removes the data association
uncertainty of the original measurement equation with the help of
a symmetric transformation. The key idea of the Kernel-SME filter
is to define a symmetric transformation that maps the measurements
to a Gaussian mixture function. This transformation is scalable to
a large number of targets and allows for deriving a Gaussian state
estimator that only has a cubic runtime complexity in the number
of targets.
Florian Faion, Marcus Baum, Uwe D. Hanebeck,
Silhouette Measurements for Bayesian Object Tracking in Noisy Point Clouds
Proceedings of the 16th International Conference on Information Fusion (Fusion 2013), Istanbul, Turkey, July 2013.
PDF
Author : Florian Faion, Marcus Baum, Uwe D. Hanebeck
Title : Silhouette Measurements for Bayesian Object Tracking in Noisy Point Clouds
In : Proceedings of the 16th International Conference on Information Fusion (Fusion 2013)
Address : Istanbul, Turkey
Date : July 2013
Abstract
In this paper, we consider the problem of jointly tracking the pose
and shape of objects based on noisy data from cameras and depth sensors.
Our proposed approach formalizes object silhouettes from image data
as measurements within a Bayesian estimation framework. Projecting
object silhouettes from images back into space yields a visual hull
that constrains the object. In this work, we focus on the 2D case.
We derive a general equation for the silhouette measurement update
that explicitly considers segmentation uncertainty of each pixel.
By assuming a bounded error for the silhouettes, we can reduce the
complexity of the general solution to only consider uncertain edges
and derive an approximate measurement update. In simulations, we
show that the proposed approach dramatically improves point-cloud-based
estimators, especially in the presence of high noise.
Igor Gilitschenski, Gerhard Kurz, Uwe D. Hanebeck,
Circular Statistics in Bearings-only Sensor Scheduling
Proceedings of the 16th International Conference on Information Fusion (Fusion 2013), Istanbul, Turkey, July 2013.
PDF
Author : Igor Gilitschenski, Gerhard Kurz, Uwe D. Hanebeck
Title : Circular Statistics in Bearings-only Sensor Scheduling
In : Proceedings of the 16th International Conference on Information Fusion (Fusion 2013)
Address : Istanbul, Turkey
Date : July 2013
Abstract
In this paper, we introduce a novel approach for scheduled tracking
of a moving target based on bearings-only sensors. Unlike classical
approaches that are typically based on the extended or unscented
Kalman filter, we rely on circular statistics to describe probability
distributions for angular measurements more accurately. As the energy
available to sensors is limited in many scenarios, we introduce a
scheduling algorithm that selects a subset of two sensors to be active
at any given time step while minimizing the uncertainty of the state
estimate. This is done by anticipating possible future measurements.
We evaluate the proposed method in simulations and compare it to
an UKF-based solution. Our evaluation demonstrates the superiority
of the presented approach, particularly when high measurement uncertainty
makes consideration of the circular geometry necessary.
Uwe D. Hanebeck,
PGF 42: Progressive Gaussian Filtering with a Twist
Proceedings of the 16th International Conference on Information Fusion (Fusion 2013), Istanbul, Turkey, July 2013.
PDF
Author : Uwe D. Hanebeck
Title : PGF 42: Progressive Gaussian Filtering with a Twist
In : Proceedings of the 16th International Conference on Information Fusion (Fusion 2013)
Address : Istanbul, Turkey
Date : July 2013
Abstract
A new Gaussian filter for estimating the state of nonlinear systems
is derived that relies on two main ingredients: i) the progressive
inclusion of the measurement information and ii) a tight coupling
between a Gaussian density and its deterministic Dirac mixture approximation.
No second Gaussian assumption for the joint density of state and
measurement is required, so that the performance is much better than
that of Linear Regression Kalman Filters (LRKFs), which heavily
rely on this assumption. In addition, the new filter directly works
with the generative system description. No Likelihood function is
required. It can be used as a plug -in replacement for standard Gaussian
filters such as the UKF.
Marco F. Huber, Uwe D. Hanebeck,
Gaussian Filtering for Polynomial Systems Based on Moment Homotopy
Proceedings of the 16th International Conference on Information Fusion (Fusion 2013), Istanbul, Turkey, July 2013.
PDF
Author : Marco F. Huber, Uwe D. Hanebeck
Title : Gaussian Filtering for Polynomial Systems Based on Moment Homotopy
In : Proceedings of the 16th International Conference on Information Fusion (Fusion 2013)
Address : Istanbul, Turkey
Date : July 2013
Abstract
This paper proposes Gaussian filters for polynomial systems with efficient
solutions for both the prediction and the filter step. For the prediction
step, computationally efficient closed-form solutions are derived
for calculating the exact moments. In order to achieve a higher estimation
quality, the filter step is solved without the usual additional assumption
that state and measurement are jointly Gaussian distributed. As this
significantly complicates the required moment calculation, a homotopy
continuation method is employed that yields almost optimal results.
Gerhard Kurz, Igor Gilitschenski, Simon J. Julier, Uwe D. Hanebeck,
Recursive Estimation of Orientation Based on the Bingham Distribution
Proceedings of the 16th International Conference on Information Fusion (Fusion 2013), Istanbul, Turkey, July 2013.
PDF
Author : Gerhard Kurz, Igor Gilitschenski, Simon J. Julier, Uwe D. Hanebeck
Title : Recursive Estimation of Orientation Based on the Bingham Distribution
In : Proceedings of the 16th International Conference on Information Fusion (Fusion 2013)
Address : Istanbul, Turkey
Date : July 2013
Abstract
Directional estimation is a common problem in many tracking applications.
Traditional filters like the Kalman filter perform poorly because
they fail to take the periodic nature of the problem into account.
We present a recursive filter for directional data based on the Bingham
distribution in two dimensions. The proposed filter can be applied
to circular filtering problems with 180 degree symmetry, i.e., rotations
by 180 degrees cannot be distinguished. It is easily implemented
using standard numerical techniques and suitable for real-time applications.
The presented approach is extensible to quaternions, which allow
tracking arbitrary three-dimensional orientations. We evaluate our
filter in a challenging scenario and compare it to a traditional
Kalman filtering approach.
Best Student Paper Award First Runner-Up Certificate (PDF)
Gerhard Kurz, Uwe D. Hanebeck,
Recursive Fusion of Noisy Depth and Position Measurements for Surface Reconstruction
Proceedings of the 16th International Conference on Information Fusion (Fusion 2013), Istanbul, Turkey, July 2013.
PDF
Author : Gerhard Kurz, Uwe D. Hanebeck
Title : Recursive Fusion of Noisy Depth and Position Measurements for Surface Reconstruction
In : Proceedings of the 16th International Conference on Information Fusion (Fusion 2013)
Address : Istanbul, Turkey
Date : July 2013
Abstract
We propose an algorithm to combine both depth and position measurements
when estimating a continuous surface. Position measurements originate
from a fixed point on the surface, whereas depth measurements are
determined by the intersection of the surface with a line originating
from the depth sensor. Through fusion of both types of measurements,
it is possible to benefit from the advantages of different sensors.
The surface is obtained through interpolation of control points with
splines, which allows a compact representation of the surface. In
order to simplify the problem of intersecting the surface with lines
originating from the depth sensor, we propose the use of polar or
spherical coordinates in surface parameterization. The presented
algorithm can be applied in both 2D and 3D settings and is independent
of the particular choice of sensors. Our method can recursively include
new information as it is obtained by using nonlinear filtering and
it considers uncertainties associated with the measurements.
Winner Best Paper Award Certificate (PDF)
Benjamin Noack, Simon J. Julier, Marc Reinhardt, Uwe D. Hanebeck,
Nonlinear Federated Filtering
Proceedings of the 16th International Conference on Information Fusion (Fusion 2013), Istanbul, Turkey, July 2013.
PDF
Author : Benjamin Noack, Simon J. Julier, Marc Reinhardt, Uwe D. Hanebeck
Title : Nonlinear Federated Filtering
In : Proceedings of the 16th International Conference on Information Fusion (Fusion 2013)
Address : Istanbul, Turkey
Date : July 2013
Abstract
The federated Kalman filter embodies an efficient and easy-to-implement
solution for linear distributed estimation problems. Data from independent
sensors can be processed locally and in parallel on different nodes
without running the risk of erroneously ignoring possible dependencies.
The underlying idea is to counteract the common process noise issue
by inflating the joint process noise matrix. In this paper, the same
trick is generalized to nonlinear models and non-Gaussian process
noise. The probability density of the joint process noise is split
into an exponential mixture of transition densities. By this means,
the process noise is modeled to independently affect the local system
models. The estimation results provided by the sensor devices can
then be fused, just as if they were indeed independent.
Florian Pfaff, Benjamin Noack, Uwe D. Hanebeck,
Data Validation in the Presence of Stochastic and Set-membership Uncertainties
Proceedings of the 16th International Conference on Information Fusion (Fusion 2013), Istanbul, Turkey, July 2013.
PDF
Author : Florian Pfaff, Benjamin Noack, Uwe D. Hanebeck
Title : Data Validation in the Presence of Stochastic and Set-membership Uncertainties
In : Proceedings of the 16th International Conference on Information Fusion (Fusion 2013)
Address : Istanbul, Turkey
Date : July 2013
Abstract
For systems suffering from different types of uncertainties, finding
criteria for validating measurements can be challenging. In this
paper, we regard both stochastic Gaussian noise with full or imprecise
knowledge about correlations and unknown but bounded errors. The
validation problems arising in the individual and combined cases
are illustrated to convey different perspectives on the proposed
conditions. Furthermore, hints are provided for the algorithmic implementation
of the validation tests. Particular focus is put on ensuring a predefined
lower bound for the probability of correctly classifying valid data.
Marc Reinhardt, Benjamin Noack, Uwe D. Hanebeck,
Advances in Hypothesizing Distributed Kalman Filtering
Proceedings of the 16th International Conference on Information Fusion (Fusion 2013), Istanbul, Turkey, July 2013.
PDF
Author : Marc Reinhardt, Benjamin Noack, Uwe D. Hanebeck
Title : Advances in Hypothesizing Distributed Kalman Filtering
In : Proceedings of the 16th International Conference on Information Fusion (Fusion 2013)
Address : Istanbul, Turkey
Date : July 2013
Abstract
In this paper, linear distributed estimation is revisited on the basis
of the hypothesizing distributed Kalman filter and equations for
a flexible application of the algorithm are derived. We propose a
new approximation for the mean-squared-error matrix and present techniques
for automatically improving the hypothesis about the global measurement
model. Utilizing these extensions, the precision of the filter is
improved so that it asymptotically yields optimal results for time-invariant
models. Pseudo-code for the implementation of the algorithm is provided
and the lossless inclusion of out-of-sequence measurements is discussed.
An evaluation demonstrates the effect of the new extensions and compares
the results to state-of-the-art methods.
Joris Sijs, Benjamin Noack, Uwe D. Hanebeck,
Event-based State Estimation with Negative Information
Proceedings of the 16th International Conference on Information Fusion (Fusion 2013), Istanbul, Turkey, July 2013.
PDF
Author : Joris Sijs, Benjamin Noack, Uwe D. Hanebeck
Title : Event-based State Estimation with Negative Information
In : Proceedings of the 16th International Conference on Information Fusion (Fusion 2013)
Address : Istanbul, Turkey
Date : July 2013
Abstract
To reduce the amount of data transfer in networked systems, measurements
are usually taken only when an event occurs rather than periodically
in time. However, this complicates estimation problems considerably
as it is not guaranteed that new sensor data will be sampled. Therefore,
an existing state estimator is extended so to cope with event-based
measurements successfully, i.e., curtail any diverging behavior in
the estimation results. To that extent, a general formulation of
event sampling is proposed. This formulation is used to set up a
state estimator combining stochastic as well as set-membership measurement
information according to a hybrid update: when an event occurs the
estimated state is updated using the stochastic measurement received
(positive information), while at periodic time instants no measurement
is received (negative information) and the update is based on knowledge
that the sensor value lies within a bounded subset of the measurement
space. An illustrative example further shows that the developed estimator
has an improved representation of estimation errors compared to a
purely stochastic estimator for various event sampling strategies.
Jannik Steinbring, Uwe D. Hanebeck,
S2KF: The Smart Sampling Kalman Filter
Proceedings of the 16th International Conference on Information Fusion (Fusion 2013), Istanbul, Turkey, July 2013.
PDF
Author : Jannik Steinbring, Uwe D. Hanebeck
Title : S2KF: The Smart Sampling Kalman Filter
In : Proceedings of the 16th International Conference on Information Fusion (Fusion 2013)
Address : Istanbul, Turkey
Date : July 2013
Abstract
An accurate Linear Regression Kalman Filter (LRKF) for nonlinear systems
called Smart Sampling Kalman Filter (S²KF) is introduced. It is based
on a new low-discrepancy Dirac Mixture approximation of Gaussian
densities. The approximation comprises an arbitrary number of optimally
and deterministically placed samples in the entire state space, so
that the filter resolution can be adapted to either achieve high-quality
results or meet computational constraints. For two samples per dimension,
the S²KF comprises the UKF as a special case. With an increasing
number of samples, the new filter quickly converges to the (typically
infeasible) exact analytic LRKF. The S²KF can be seen as the ultimate
generalization of all sample-based LRKFs such as the UKF, sigma-point
filters, higher-order variants etc., as it homogeneously covers the
state space with an arbitrary number of samples. It is evaluated
by performing extended target tracking.
Antonio Zea, Florian Faion, Marcus Baum, Uwe D. Hanebeck,
Level-Set Random Hyper Surface Models for Tracking Non-Convex Extended Objects
Proceedings of the 16th International Conference on Information Fusion (Fusion 2013), Istanbul, Turkey, July 2013.
PDF
Author : Antonio Zea, Florian Faion, Marcus Baum, Uwe D. Hanebeck
Title : Level-Set Random Hyper Surface Models for Tracking Non-Convex Extended Objects
In : Proceedings of the 16th International Conference on Information Fusion (Fusion 2013)
Address : Istanbul, Turkey
Date : July 2013
Abstract
This paper presents a novel approach to track a non-convex shape approximation
of an extended target based on noisy point measurements. For this
purpose, a novel type of Random Hypersurface Model (RHM), called
Level-Set RHM is introduced that models the interior of a shape with
level-sets of an implicit function. Based on the Level-Set RHM, a
nonlinear measurement equation can be derived that allows to employ
a standard Gaussian state estimator for tracking an extended object
even in scenarios with high measurement noise. In this paper, shapes
are described using polygons and shape regularization is applied
using ideas from active contour models.
Jörg Fischer, Marc Reinhardt, Uwe D. Hanebeck,
Optimal Sequence-Based Control and Estimation of Networked Linear Systems
at - Automatisierungstechnik, Oldenbourg Verlag, 61(7):467-476, July 2013.
PDF URL
Author : Jörg Fischer, Marc Reinhardt, Uwe D. Hanebeck
Title : Optimal Sequence-Based Control and Estimation of Networked Linear Systems
In : at - Automatisierungstechnik, Oldenbourg Verlag
Address :
Date : July 2013
Abstract
In this paper, a unified approach to sequence-based control and estimation
of linear networked systems with multiple sensors is proposed. Time
delays and data losses in the controller-actuator link are compensated
by sending sequences of control inputs. The sequence-based design
paradigm is further extended to the sensor-controller connections
without increasing the load of the network. In this context, we present
a recursive solution based on the Hypothesizing Distributed Kalman
Filter (HKF) that is included in the overall sequence-based controller
design.
Jörg Fischer, Achim Hekler, Maxim Dolgov, Uwe D. Hanebeck,
Optimal Sequence-Based LQG Control over TCP-like Networks Subject to Random Transmission Delays and Packet Losses
Proceedings of the 2013 American Control Conference (ACC 2013), Washington D. C., USA, June 2013.
PDF
Author : Jörg Fischer, Achim Hekler, Maxim Dolgov, Uwe D. Hanebeck
Title : Optimal Sequence-Based LQG Control over TCP-like Networks Subject to Random Transmission Delays and Packet Losses
In : Proceedings of the 2013 American Control Conference (ACC 2013)
Address : Washington D. C., USA
Date : June 2013
Abstract
This paper addresses the problem of sequence-based controller design
for Networked Control Systems (NCS), where control inputs and measurements
are transmitted over TCP-like network connections that are subject
to random transmission delays and packet losses. To cope with the
network effects, the controller not only sends the current control
input to the actuator, but also a sequence of predicted control inputs
at every time step. In this setup, we derive an optimal solution
to the Linear Quadratic Gaussian (LQG) control problem and prove
that the separation principle holds. Simulations demonstrate the
improved performance of this optimal controller compared to other
sequence-based approaches.
Igor Gilitschenski, Uwe D. Hanebeck,
Efficient Deterministic Dirac Mixture Approximation
Proceedings of the 2013 American Control Conference (ACC 2013), Washington D. C., USA, June 2013.
PDF
Author : Igor Gilitschenski, Uwe D. Hanebeck
Title : Efficient Deterministic Dirac Mixture Approximation
In : Proceedings of the 2013 American Control Conference (ACC 2013)
Address : Washington D. C., USA
Date : June 2013
Abstract
We propose an efficient method for approximating arbitrary Gaussian
densities by a mixture of Dirac components. This approach is based
on the modification of the classical Cramér-von Mises distance, which
is adapted to the multivariate scenario by using Localized Cumulative
Distributions (LCDs) as a replacement for the cumulative distribution
function. LCDs consider the local probabilistic influence of aprobability
density around a given point. Our modification of the Cramér-von
Mises distance can be approximated for certain special cases in closed-form.
The created measure is minimized in order to compute the positions
of the Dirac components for a standard normal distribution.
Gerhard Kurz, Igor Gilitschenski, Uwe D. Hanebeck,
Recursive Nonlinear Filtering for Angular Data Based on Circular Distributions
Proceedings of the 2013 American Control Conference (ACC 2013), Washington D. C., USA, June 2013.
PDF
Author : Gerhard Kurz, Igor Gilitschenski, Uwe D. Hanebeck
Title : Recursive Nonlinear Filtering for Angular Data Based on Circular Distributions
In : Proceedings of the 2013 American Control Conference (ACC 2013)
Address : Washington D. C., USA
Date : June 2013
Abstract
Estimation of circular quantities is a widespread problem that occurs
in many tracking and control applications. Commonly used approaches
such as the Kalman filter, the extended Kalman filter (EKF), and
the unscented Kalman filter (UKF) do not take periodicity explicitly
into account, which can result in low estimation accuracy. We present
a filtering algorithm for angular quantities in nonlinear systems
that is based on circular statistics. The new filter switches between
three different representations of probability distributions on the
circle, the wrapped normal, the von Mises, and a Dirac mixture density.
It can be seen as a systematic generalization of the UKF to circular
statistics. We evaluate the proposed filter in simulations and show
its superiority to conventional approaches.
Marcus Baum, Uwe D. Hanebeck,
Extended Object Tracking with Random Hypersurface Models (to appear)
IEEE Transactions on Aerospace and Electronic Systems, 2013.
Author : Marcus Baum, Uwe D. Hanebeck
Title : Extended Object Tracking with Random Hypersurface Models (to appear)
In : IEEE Transactions on Aerospace and Electronic Systems
Address :
Date : 2013

Publikationen aus dem Jahr 2012

Marcus Baum, Patrick Ruoff, Dominik Itte, Uwe D. Hanebeck,
Optimal Point Estimates for Multi-target States based on Kernel Distances
Proceedings of the 51st IEEE Conference on Decision and Control (CDC 2012), Maui, Hawaii, USA, December 2012.
PDF
Author : Marcus Baum, Patrick Ruoff, Dominik Itte, Uwe D. Hanebeck
Title : Optimal Point Estimates for Multi-target States based on Kernel Distances
In : Proceedings of the 51st IEEE Conference on Decision and Control (CDC 2012)
Address : Maui, Hawaii, USA
Date : December 2012
Abstract
Almost all multi-target tracking systems have to generate point estimates
for the targets, e.g., for displaying the tracks. The novel idea
in this paper is to consider point estimates for multi-target states
that are optimal according to a kernel distance measure. Because
the kernel distance is a metric on point sets and ignores the target
labels, shortcomings of Minimum Mean Squared Error (MMSE) estimates
for multi-target states can be avoided. We show how the calculation
of these point estimates can be casted as an optimization problem
and it turns out that it corresponds to the problem of reducing the
Probability Hypothesis Density (PHD) function to a Dirac mixture
density. Finally, we discuss a generalization of the kernel distance
called LCD distance, which does not require to choose a specific
kernel width. The presented methods are evaluated in a Multiple-Hypotheses
Tracker (MHT) setting with up to ten targets.
Christof Chlebek, Achim Hekler, Uwe D. Hanebeck,
Stochastic Nonlinear Model Predictive Control Based on Progressive Density Simplification
Proceedings of the 51st IEEE Conference on Decision and Control (CDC 2012), Maui, Hawaii, USA, December 2012.
PDF
Author : Christof Chlebek, Achim Hekler, Uwe D. Hanebeck
Title : Stochastic Nonlinear Model Predictive Control Based on Progressive Density Simplification
In : Proceedings of the 51st IEEE Conference on Decision and Control (CDC 2012)
Address : Maui, Hawaii, USA
Date : December 2012
Abstract
Increasing demand for Nonlinear Model Predictive Control with the
ability to handle highly noise-corrupted systems has recently given
rise to stochastic control approaches. Besides providing high-quality
results within a noisy environment, these approaches have one problem
in common, namely a high computational demand and, as a consequence,
generally a short prediction horizon. In this paper, we propose to
reduce the computational complexity of prediction and value function
evaluation within the control horizon by simplifying the system progressively
down to the deterministic case. Approximation of occurring probability
densities by a specific representation, the deterministic Dirac mixture
density, with a decreasing resolution (i.e., approximation quality)
leads via natural decomposition to a point estimate and thus, can
be treated in a deterministic manner. Hence, calculation of the remaining
time steps requires considerably less computation time.
Achim Hekler, Jörg Fischer, Uwe D. Hanebeck,
Sequence-Based Control for Networked Control Systems Based on Virtual Control Inputs
Proceedings of the 51st IEEE Conference on Decision and Control (CDC 2012), Maui, Hawaii, USA, December 2012.
PDF
Author : Achim Hekler, Jörg Fischer, Uwe D. Hanebeck
Title : Sequence-Based Control for Networked Control Systems Based on Virtual Control Inputs
In : Proceedings of the 51st IEEE Conference on Decision and Control (CDC 2012)
Address : Maui, Hawaii, USA
Date : December 2012
Abstract
In this paper, we address the problem of controlling a system over
an unreliable UDP-like network that is affected by time-varying delays
and randomly occurring packet losses. A major challenge of this setup
is that the controller just has uncertain information about the control
inputs actually applied by the actuator. The key idea of this work
is to model the uncertain control inputs by random variables, the
so-called virtual control inputs, which are characterized by discrete
probability density functions. Subject to this probabilistic description,
a novel, easy to implement sequencebased control approach is proposed
that extends any given state feedback controller designed without
consideration of the network-induced disturbances. The high performance
of the proposed controller is demonstrated by means of Monte Carlo
simulation runs with an inverted pendulum on a cart.
Benjamin Noack, Florian Pfaff, Uwe D. Hanebeck,
Optimal Kalman Gains for Combined Stochastic and Set-Membership State Estimation
Proceedings of the 51st IEEE Conference on Decision and Control (CDC 2012), Maui, Hawaii, USA, December 2012.
PDF
Author : Benjamin Noack, Florian Pfaff, Uwe D. Hanebeck
Title : Optimal Kalman Gains for Combined Stochastic and Set-Membership State Estimation
In : Proceedings of the 51st IEEE Conference on Decision and Control (CDC 2012)
Address : Maui, Hawaii, USA
Date : December 2012
Abstract
In state estimation theory, two directions are mainly followed in
order to model disturbances and errors. Either uncertainties are
modeled as stochastic quantities or they are characterized by their
membership to a set. Both approaches have distinct advantages and
disadvantages making each one inherently better suited to model different
sources of estimation uncertainty. This paper is dedicated to the
task of combining stochastic and set-membership estimation methods.
A Kalman gain is derived that minimizes the mean squared error in
the presence of both stochastic and additional unknown but bounded
uncertainties, which are represented by Gaussian random variables
and ellipsoidal sets, respectively. As a result, a generalization
of the well-known Kalman filtering scheme is attained that reduces
to the standard Kalman filter in the absence of set-membership uncertainty
and that otherwise becomes the intersection of sets in case of vanishing
stochastic uncertainty. The proposed concept also allows to prioritize
either the minimization of the stochastic uncertainty or the minimization
of the set-membership uncertainty.
Marc Reinhardt, Benjamin Noack, Uwe D. Hanebeck,
Decentralized Control Based on Globally Optimal Estimation
Proceedings of the 51st IEEE Conference on Decision and Control (CDC 2012), Maui, Hawaii, USA, December 2012.
PDF
Author : Marc Reinhardt, Benjamin Noack, Uwe D. Hanebeck
Title : Decentralized Control Based on Globally Optimal Estimation
In : Proceedings of the 51st IEEE Conference on Decision and Control (CDC 2012)
Address : Maui, Hawaii, USA
Date : December 2012
Abstract
A new method for globally optimal estimation in decentralized sensor-networks
is applied to the decentralized control problem. The resulting approach
is proven to be optimal when the nodes have access to all information
in the network. More precisely, we utilize an algorithm for optimal
distributed estimation in order to obtain local estimates whose combination
yields the globally optimal estimate. When the interconnectivity
is high, the local estimates are almost optimal, which motivates
the application of the principle of separation. Thus, we optimize
the controller and finally obtain a flexible algorithm, whose quality
is evaluated in different scenarios. In applications where the strong
requirements on a perfect communication cannot be guaranteed, we
derive quality bounds by help of a detailed evaluation of the algorithm.
When information is regularly exchanged, it is demonstrated that
the algorithm performs almost optimally and therefore, offers system
designers a flexible and easy to implement approach. The field of
applications lies within the area of strongly networked systems,
in particular, when communication disturbances cannot be foreseen
or when the network structure is too complicated to apply optimized
regulators.
Gerhard Kurz, Uwe D. Hanebeck,
Image Stabilization with Model-Based Tracking for Beating Heart Surgery
11. Jahrestagung der Deutschen Gesellschaft für Computer- und Roboterassistierte Chirurgie (CURAC12), Düsseldorf, Germany, November 2012.
PDF
Author : Gerhard Kurz, Uwe D. Hanebeck
Title : Image Stabilization with Model-Based Tracking for Beating Heart Surgery
In : 11. Jahrestagung der Deutschen Gesellschaft für Computer- und Roboterassistierte Chirurgie (CURAC12)
Address : Düsseldorf, Germany
Date : November 2012
Abstract
Performing surgery on the beating heart has significant advantages
compared to cardiopulmonary bypass. However, when performed directly,
it is very demanding for the surgeon. As an alternative, using a
teleoperated robot for compensating the heart motion has been proposed.
As an addition, this paper describes how stabilized images are obtained
to create the illusion of operating on a stationary heart. For that
purpose, the heart motion is tracked with a stochastic physical model.
Based on correspondences obtained by motion tracking, image stabilization
is considered as a scattered data interpolation problem. The proposed
algorithms are evaluated on a heart phantom and in in-vivo experiments
on a porcine heart, which show that there is very little residual
motion in the stabilized images.
Marcus Baum, Uwe D. Hanebeck,
Extended Object Tracking Based on Set-Theoretic and Stochastic Fusion
IEEE Transactions on Aerospace and Electronic Systems, 48(4):3103-3115, October 2012.
PDF URL
Author : Marcus Baum, Uwe D. Hanebeck
Title : Extended Object Tracking Based on Set-Theoretic and Stochastic Fusion
In : IEEE Transactions on Aerospace and Electronic Systems
Address :
Date : October 2012
Abstract
A novel approach for extended object tracking is presented. In contrast
to existing approaches, no statistical assumptions about the location
of the measurement sources on the extended target object are made.
As a consequence, a combined set-theoretic and stochastic estimator
is obtained that is robust to systematic errors in the target model.
The benefits of the new approach is demonstrated by means of simulations.
Florian Faion, Simon Friedberger, Antonio Zea, Uwe D. Hanebeck,
Intelligent Sensor-Scheduling for Multi-Kinect-Tracking
Proceedings of the 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2012), Vilamoura, Algarve, Portugal, October 2012.
PDF
Author : Florian Faion, Simon Friedberger, Antonio Zea, Uwe D. Hanebeck
Title : Intelligent Sensor-Scheduling for Multi-Kinect-Tracking
In : Proceedings of the 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2012)
Address : Vilamoura, Algarve, Portugal
Date : October 2012
Abstract
This paper describes a method to intelligently schedule a network
of multiple RGBD sensors in a Bayesian object tracking scenario,
with special focus on Microsoft Kinect devices. These setups have
issues such as the large amount of raw data generated by the sensors
and interference caused by overlapping fields of view. The proposed
algorithm addresses these issues by selecting and exclusively activating
the sensor that yields the best measurement, as defined by a novel
stochastic model that also considers hardware constraints and intrinsic
parameters. In addition, as existing solutions to toggle the sensors
were found to be insufficient, the development of a hardware module,
especially designed for quick toggling and synchronization with the
depth stream, is also discussed. The algorithm then is evaluated
within the scope of a multi-Kinect object tracking scenario and compared
to other scheduling strategies.
Marcus Baum, Florian Faion, Uwe D. Hanebeck,
Tracking Ground Moving Extended Objects using RGBD Data
Proceedings of the 2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2012), Hamburg, Germany, September 2012.
PDF
Author : Marcus Baum, Florian Faion, Uwe D. Hanebeck
Title : Tracking Ground Moving Extended Objects using RGBD Data
In : Proceedings of the 2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2012)
Address : Hamburg, Germany
Date : September 2012
Abstract
This paper is about an experimental set-up for tracking a ground moving
mobile object from a bird's eye view. In this experiment, an RGB
and depth camera is used for detecting moving points. The detected
points serve as input for a probabilistic extended object tracking
algorithm that simultaneously estimates the kinematic parameters
and the shape parameters of the object. By this means, it is easy
to discriminate moving objects from the background and the probabilistic
tracking algorithm ensures a robust and smooth shape estimate. We
provide an experimental evaluation of a recent Bayesian extended
object tracking algorithm based on a so-called Random Hypersurface
Model and give a comparison with active contour models.
Nominee Best Student Paper Award
Marc Reinhardt, Benjamin Noack, Uwe D. Hanebeck,
The Hypothesizing Distributed Kalman Filter
Proceedings of the 2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2012), Hamburg, Germany, September 2012.
PDF
Author : Marc Reinhardt, Benjamin Noack, Uwe D. Hanebeck
Title : The Hypothesizing Distributed Kalman Filter
In : Proceedings of the 2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2012)
Address : Hamburg, Germany
Date : September 2012
Abstract
This paper deals with distributed information processing in sensor
networks. We propose the Hypothesizing Distributed Kalman Filter
that incorporates an assumption of the global measurement model into
the distributed estimation process. The procedure is based on the
Distributed Kalman Filter and inherits its optimality when the assumption
about the global measurement uncertainty is met. Recursive formulas
for local processing as well as for fusion are derived. We show that
the proposed algorithm yields the same results, no matter whether
the measurements are processed locally or globally, even when the
process noise is not negligible. For further processing of the estimates,
a consistent bound for the error covariance matrix is derived. All
derivations and explanations are illustrated by means of a new classification
scheme for estimation processes.
Nominee Best Student Paper Award
Marc Peter Deisenroth, Ryan Darby Turner, Marco F. Huber, Uwe D. Hanebeck, Carl Edward Rasmussen,
Robust Filtering and Smoothing with Gaussian Processes
IEEE Transactions on Automatic Control, 57(7):1865-1871, July 2012.
PDF URL
Author : Marc Peter Deisenroth, Ryan Darby Turner, Marco F. Huber, Uwe D. Hanebeck, Carl Edward Rasmussen
Title : Robust Filtering and Smoothing with Gaussian Processes
In : IEEE Transactions on Automatic Control
Address :
Date : July 2012
Abstract
We propose a principled algorithm for robust Bayesian filtering and
smoothing in nonlinear stochastic dynamic systems when both the transition
function and the measurement function are described by non-parametric
Gaussian process (GP) models. GPs are gaining increasing importance
in signal processing, machine learning, robotics, and control for
representing unknown system functions by posterior probability distributions.
This modern way of system identification is more robust than finding
point estimates of a parametric function representation. Our principled
filtering/smoothing approach for GP dynamic systems is based on analytic
moment matching in the context of the forward-backward algorithm.
Our numerical evaluations demonstrate the robustness of the proposed
approach in situations where other state-of-the-art Gaussian filters
and smoothers can fail.
Marcus Baum, Florian Faion, Uwe D. Hanebeck,
Modeling the Target Extent with Multiplicative Noise
Proceedings of the 15th International Conference on Information Fusion (Fusion 2012), Singapore, July 2012.
PDF
Author : Marcus Baum, Florian Faion, Uwe D. Hanebeck
Title : Modeling the Target Extent with Multiplicative Noise
In : Proceedings of the 15th International Conference on Information Fusion (Fusion 2012)
Address : Singapore
Date : July 2012
Abstract
Extended target tracking deals with simultaneously tracking the shape
and the kinematic parameters of a target. In this work, we formulate
the extended target tracking problem as a state estimation problem
with both multiplicative and additive measurement noise. In case
of extended targets with known orientation, we show that the best
linear estimator is not consistent and, hence, is unsuitable for
this problem. In order to overcome this issue, we propose a quadratic
estimator for a recursive closed-form measurement update. Simulations
demonstrate the performance of the estimator.
Marcus Baum, Peter Willett, Uwe D. Hanebeck,
Calculating Some Exact MMOSPA Estimates for Particle Distributions
Proceedings of the 15th International Conference on Information Fusion (Fusion 2012), Singapore, July 2012.
PDF
Author : Marcus Baum, Peter Willett, Uwe D. Hanebeck
Title : Calculating Some Exact MMOSPA Estimates for Particle Distributions
In : Proceedings of the 15th International Conference on Information Fusion (Fusion 2012)
Address : Singapore
Date : July 2012
Abstract
In this work, we present some exact algorithms for calculating the
minimum mean optimal sub-pattern assignment (MMOSPA) estimate for
probability densities that are represented with particles. First,
a polynomial-time algorithm for two targets is derived by reducing
the problem to the enumeration of the cells of a hyperplane arrangement.
Second, we present a linear-time algorithm for an arbitrary number
of scalar target states, which is based on the insight that the MMOSPA
estimate coincides with the mean of the order statistics.
Florian Faion, Marcus Baum, Uwe D. Hanebeck,
Tracking 3D Shapes in Noisy Point Clouds with Random Hypersurface Models
Proceedings of the 15th International Conference on Information Fusion (Fusion 2012), Singapore, July 2012.
PDF
Author : Florian Faion, Marcus Baum, Uwe D. Hanebeck
Title : Tracking 3D Shapes in Noisy Point Clouds with Random Hypersurface Models
In : Proceedings of the 15th International Conference on Information Fusion (Fusion 2012)
Address : Singapore
Date : July 2012
Abstract
Depth sensors such as the Microsoft Kinect\\\\\\\\texttrademark depth sensor
provide three dimensional point clouds of an observed scene. In this
paper, we employ Random Hypersurface Models (RHMs), which is a modeling
technique for extended object tracking, to point cloud fusion in
order to track a shape approximation of an underlying object. We
present a novel variant of RHMs to model shapes in 3D space. Based
on this novel model, we develop a specialized algorithm to track
persons by approximating their shapes as cylinders. For evaluation,
we utilize a Kinect network and simulations based on a stochastic
sensor model.
Florian Faion, Patrick Ruoff, Antonio Zea, Uwe D. Hanebeck,
Recursive Bayesian Calibration of Depth Sensors with Non-Overlapping Views
Proceedings of the 15th International Conference on Information Fusion (Fusion 2012), Singapore, July 2012.
PDF
Author : Florian Faion, Patrick Ruoff, Antonio Zea, Uwe D. Hanebeck
Title : Recursive Bayesian Calibration of Depth Sensors with Non-Overlapping Views
In : Proceedings of the 15th International Conference on Information Fusion (Fusion 2012)
Address : Singapore
Date : July 2012
Abstract
In this paper we present a recursive Bayesian method to calibrate
rigidly linked depth sensors with non-overlapping fields of view.
The extrinsic parameters of this setup are obtained by rotating and
translating both cameras, estimating the local transformations using
point feature correspondences, and finally using these values to
recursively find a solution to the matrix equation $\\\\\\\\matA_k\\\\\\\\matX=\\\\\\\\matX\\\\\\\\matB_k$.
The algorithm is based on a Bayesian estimator, which allows the
consideration of camera-specific measurement noise and permits the
system to adapt naturally to changes in the extrinsic parameters.
The derived equations were carefully chosen to be free from singularities.
This paper also includes a thorough evaluation based on synthetic
and real data to show the effectiveness of the algorithm.
Jörg Fischer, Achim Hekler, Uwe D. Hanebeck,
State Estimation in Networked Control Systems
Proceedings of the 15th International Conference on Information Fusion (Fusion 2012), Singapore, July 2012.
PDF
Author : Jörg Fischer, Achim Hekler, Uwe D. Hanebeck
Title : State Estimation in Networked Control Systems
In : Proceedings of the 15th International Conference on Information Fusion (Fusion 2012)
Address : Singapore
Date : July 2012
Abstract
We consider the problem of state estimation in a Networked Control
System, where measurements and control inputs are transmitted via
a communication network. The network is subject to time-varying delays
and stochastic data losses and does not provide acknowledgments of
successfully transmitted data packets. A challenge that arises in
this configuration is that the estimator has only uncertain information
about the actually applied control inputs. In this paper, we derive
a multiple-model based estimator that uses the state measurements
to estimate the applied control inputs so that the overall state
estimation is improved. The efficiency of the proposed approach is
demonstrated by means of Monte-Carlo-Simulation runs with an inverted
pendulum on a cart.
Igor Gilitschenski, Uwe D. Hanebeck,
A Robust Computational Test for Overlap of Two Arbitrary-dimensional Ellipsoids in Fault-Detection of Kalman Filters
Proceedings of the 15th International Conference on Information Fusion (Fusion 2012), Singapore, July 2012.
PDF
Author : Igor Gilitschenski, Uwe D. Hanebeck
Title : A Robust Computational Test for Overlap of Two Arbitrary-dimensional Ellipsoids in Fault-Detection of Kalman Filters
In : Proceedings of the 15th International Conference on Information Fusion (Fusion 2012)
Address : Singapore
Date : July 2012
Abstract
On-line fault-detection in uncertain measurement and estimation systems
is of particular interest in many applications. In certain systems
based on the Kalman filter, this test can be performed by checking
whether hyperellipsoids overlap. This test can be applied to detecting
failure in the system itself or in the sensors used to determine
the system state. To facilitate the practical application of such
tests, we describe a simple condition for overlap of two ellipsoids
and propose an efficient algorithmic implementation for testing this
condition. There are applications in many other areas, such as collision
avoidance or computer graphics. Our proposal makes use of Leverriere's
algorithm and Sturm's theorem, a result of algebraic geometry. Thus,
no approximative methods, such as root finding or minimization are
needed. Furthermore, the complexity of the algorithm is fixed for
a fixed problem dimension.
Uwe D. Hanebeck, Jannik Steinbring,
Progressive Gaussian Filtering Based on Dirac Mixture Approximations
Proceedings of the 15th International Conference on Information Fusion (Fusion 2012), Singapore, July 2012.
PDF
Author : Uwe D. Hanebeck, Jannik Steinbring
Title : Progressive Gaussian Filtering Based on Dirac Mixture Approximations
In : Proceedings of the 15th International Conference on Information Fusion (Fusion 2012)
Address : Singapore
Date : July 2012
Abstract
In this paper, we propose a progressive Gaussian filter, where the
measurement information is continuously included into the given prior
estimate (although we perform observations at discrete time steps).
The key idea is to derive a system of ordinary first-order differential
equations (ODE) that is used for continuously tracking the true non-Gaussian
posterior by its best matching Gaussian approximation. Calculating
the required moments of the true posterior is performed based on
corresponding Dirac Mixture approximations. The performance of the
new filter is evaluated in comparison with state-of-the-art filters
by means of a canonical benchmark example, the discrete-time cubic
sensor problem.
Achim Hekler, Jörg Fischer, Uwe D. Hanebeck,
Control over Unreliable Networks Based on Control Input Densities
Proceedings of the 15th International Conference on Information Fusion (Fusion 2012), Singapore, July 2012.
PDF
Author : Achim Hekler, Jörg Fischer, Uwe D. Hanebeck
Title : Control over Unreliable Networks Based on Control Input Densities
In : Proceedings of the 15th International Conference on Information Fusion (Fusion 2012)
Address : Singapore
Date : July 2012
Abstract
Time delays and data losses arising from an unreliable communication
between the components of a control loop decrease the quality of
control and thus, have to be incorporated explicitly in the control
decision. In this paper, a novel concept, the so-called virtual control
inputs, is presented, which extends the well-established control
technique of sending sequences of future inputs by incorporating
also the potential effects of previously transmitted sequences on
the future system behavior. The key idea of this paper is to model
the unknown future inputs as random variables characterized by probability
density functions over the finite set of potential future inputs.
Subject to this probabilistic description of the future inputs, the
controller determines the optimal open-loop sequence over a finite
horizon. The high capacity of the proposed approach is demonstrated
by simulations, in which a sensor manager schedules sensors for tracking
a mobile object.
Benjamin Noack, Florian Pfaff, Uwe D. Hanebeck,
Combined Stochastic and Set-membership Information Filtering in Multisensor Systems
Proceedings of the 15th International Conference on Information Fusion (Fusion 2012), Singapore, July 2012.
PDF
Author : Benjamin Noack, Florian Pfaff, Uwe D. Hanebeck
Title : Combined Stochastic and Set-membership Information Filtering in Multisensor Systems
In : Proceedings of the 15th International Conference on Information Fusion (Fusion 2012)
Address : Singapore
Date : July 2012
Abstract
In state estimation theory, stochastic and set-membership approaches
are generally considered separately from each other. Both concepts
have distinct advantages and disadvantages making each one inherently
better suited to model different sources of estimation uncertainty.
In order to better utilize the potentials of both concepts, the core
element of this paper is a Kalman filtering scheme that allows for
a simultaneous treatment of stochastic and set-membership uncertainties.
An uncertain quantity is herein modeled by a set of Gaussian densities.
Since many modern applications operate in networked systems that
may consist of a multitude of local processing units and sensor nodes,
estimates have to be computed in a distributed manner and measurements
may arrive at high frequency. An algebraic reformulation of the Kalman
filter, the information filter, significantly eases the implementation
of such distributed fusion architectures. This paper explicates how
stochastic and set-membership uncertainties can simultaneously be
treated within this information form and compared to the Kalman filter,
it becomes apparent that the quality of some required approximations
is enhanced.
Ferdinand Packi, Uwe D. Hanebeck,
Robust NLOS Discrimination for Range-Based Acoustic Pose Tracking
Proceedings of the 15th International Conference on Information Fusion (Fusion 2012), Singapore, July 2012.
PDF
Author : Ferdinand Packi, Uwe D. Hanebeck
Title : Robust NLOS Discrimination for Range-Based Acoustic Pose Tracking
In : Proceedings of the 15th International Conference on Information Fusion (Fusion 2012)
Address : Singapore
Date : July 2012
Abstract
Indoor localization is a field in research with many competing technologies
using different kinds of media. A common challenge faced by most
systems is dealing with Non-Line-of-Sight (NLOS) conditions. We are
addressing this issue with focus on sound in the frequency range
above 20 kHz, as we encountered severe occurrence of outliers due
to multipath propagation, by reflections, and from occlusion. The
proper discrimination of erroneous signals is of special concern
during initialization time of the tracking system. During run time,
the computationally demanding process can be spared, if motion is
modelled and stochastic filtering techniques are applied. This paper
depicts solutions for both cases, and demonstrates that a combined
use of static and dynamic localization methods delivers increased
robustness at an affordable computational cost.
Marc Reinhardt, Benjamin Noack, Uwe D. Hanebeck,
On Optimal Distributed Kalman Filtering in Non-ideal Situations
Proceedings of the 15th International Conference on Information Fusion (Fusion 2012), Singapore, July 2012.
PDF
Author : Marc Reinhardt, Benjamin Noack, Uwe D. Hanebeck
Title : On Optimal Distributed Kalman Filtering in Non-ideal Situations
In : Proceedings of the 15th International Conference on Information Fusion (Fusion 2012)
Address : Singapore
Date : July 2012
Abstract
The distributed processing of measurements and the subsequent data
fusion is called Track-to-Track fusion. Although a solution for the
Track-to-Track fusion that is equivalent to a central processing
scheme has been proposed, this algorithm suffers from strict requirements
regarding the local availability of knowledge about utilized models
of the remote nodes. By means of simple examples, we investigate
the effects of incorrectly assumed models and trace the errors back
to a bias, which we derive in closed form. We propose an extension
to the exact Track-to-Track fusion algorithm that corrects the bias
after arbitrarily many time steps. This new approach yields optimal
results when the assumptions about the measurement models are correct
and otherwise still provides the exact value for the mean-squared-error
matrix. The performance of this algorithm is demonstrated and applications
are presented that, e.g., allow the employment of nonlinear filter
methods.
Marc Reinhardt, Benjamin Noack, Uwe D. Hanebeck,
Closed-form Optimization of Covariance Intersection for Low-dimensional Matrices
Proceedings of the 15th International Conference on Information Fusion (Fusion 2012), Singapore, July 2012.
PDF
Author : Marc Reinhardt, Benjamin Noack, Uwe D. Hanebeck
Title : Closed-form Optimization of Covariance Intersection for Low-dimensional Matrices
In : Proceedings of the 15th International Conference on Information Fusion (Fusion 2012)
Address : Singapore
Date : July 2012
Abstract
The fusion under unknown correlations is an important technique in
sensor-network information processing as the cross-correlations between
different estimates remain often unknown to the nodes. Covariance
intersection is a wide-spread and efficient algorithm to fuse estimates
under such uncertain conditions. Although different optimization
criteria have been developed, the trace or determinant minimization
of the fused covariance matrix seems to be most meaningful. However,
this minimization requires numeric solutions of a convex optimization
problem. We derive an algorithm to reduce this nonlinear optimization
to the well-known polynomial root-finding problem. This allows us
to present closed-form solutions for the determinant criterion when
the dimension of the occurring covariance matrices is at most 4 and
for the trace criterion when the dimension of the covariance matrices
is at most 3. We demonstrate the effectiveness of the approach by
means of a speed evaluation.
Achim Hekler, Christof Chlebek, Uwe D. Hanebeck,
Open-Loop Feedback Control of Nonlinear Stochastic Systems Based on Deterministic Dirac Mixture Densities
Proceedings of the 2012 American Control Conference (ACC 2012), Montréal, Canada, June 2012.
PDF
Author : Achim Hekler, Christof Chlebek, Uwe D. Hanebeck
Title : Open-Loop Feedback Control of Nonlinear Stochastic Systems Based on Deterministic Dirac Mixture Densities
In : Proceedings of the 2012 American Control Conference (ACC 2012)
Address : Montréal, Canada
Date : June 2012
Abstract
The main problem of stochastic nonlinear model predictive control
(SNMPC) is that the equations for state prediction and calculation
of the expected reward are in general not solvable in closed form.
A popular approach is to approximate the occurring continuous probability
density functions by a discrete density representation, which allows
an analytical solution of the SNMPC equations. In this paper, we
propose to draw the samples not randomly as in Monte Carlo based
methods, but systematically by minimizing a distance measure. In
doing so, fewer components are generally required to represent the
underlying probability density while achieving the same approximation
quality. Especially if the evaluation of the expected reward is computationally
expensive, this property affects the complexity of computation significantly.
By means of a path planning problem, we have substantiated this statement
with several simulation runs.
Daniel Lyons, Jan-Peter Calliess, Uwe D. Hanebeck,
Chance Constrained Model Predictive Control for Multi-Agent Systems with Coupling Constraints
Proceedings of the 2012 American Control Conference (ACC 2012), Montréal, Canada, June 2012.
PDF
Author : Daniel Lyons, Jan-Peter Calliess, Uwe D. Hanebeck
Title : Chance Constrained Model Predictive Control for Multi-Agent Systems with Coupling Constraints
In : Proceedings of the 2012 American Control Conference (ACC 2012)
Address : Montréal, Canada
Date : June 2012
Abstract
We consider stochastic model predictive control of a multi-agent systems
with constraints on the probabilities of inter-agent collisions.
First, we propose a method based on sample average approximation
of the collision probabilities to make the stochastic control problem
computationally tractable. Its approximation error vanishes for fixed
control inputs as the number of samples goes to infinity. However,
empirical results indicate that the complexity of the resulting optimization
problem can be too high to be solved under under real-time requirements.
To alleviate the computational burden we propose a second approach
that uses probabilistic bounds to determine regions of increased
probability of presence for each agent and introduce constraints
for the control problem prohibiting overlap of these regions. We
prove that the resulting problem is conservative for the original
problem, i.e., every control strategy that is feasible under our
new constraints will automatically be feasible for the true original
problem. Furthermore, we present simulations demonstrating better
run-time performance of our second approach compared to the sample
average approximation. Finally, we empirically show that our approach
is better suited than robust control approaches for situations in
which the systems under control are affected by stochastic disturbances.
Yvonne Fischer, Marcus Baum, Fabian Flohr, Uwe Hanebeck, Jürgen Beyerer,
Evaluation of Tracking Methods for Maritime Surveillance
Signal Processing, Sensor Fusion, and Target Recognition XXI (Proceedings of SPIE), Baltimore, Maryland, USA, April 2012.
URL
Author : Yvonne Fischer, Marcus Baum, Fabian Flohr, Uwe Hanebeck, Jürgen Beyerer
Title : Evaluation of Tracking Methods for Maritime Surveillance
In : Signal Processing, Sensor Fusion, and Target Recognition XXI (Proceedings of SPIE)
Address : Baltimore, Maryland, USA
Date : April 2012
Abstract
In this article we present an evaluation of different target tracking
methods based on various simulated scenarios in the maritime domain.
We implemented well known algorithms (JIPDA, Linear Multi Target
PDA, Linear Joint PDA, Monte Carlo Markov Chain Data Association)
and integrated them into a data fusion architecture. The algorithms
have been compared based on extensions of the Optimal Subpattern
Assignment metric. Also further performance measures are used to
get a single score for each algorithm. As no single algorithm is
equally well fitted to all tested scenarios, our results show which
algorithms fits best for specific scenarios.
Tobias Kretz, Antonia Pérez Arias, Simon Friedberger, Uwe D. Hanebeck,
Using Extended Range Telepresence to Collect Data of Pedestrian Dynamics
Proceedings of the Transportation Research Board 91st Annual Meeting (TRB 2012), Washington D. C., USA, January 2012.
PDF
Author : Tobias Kretz, Antonia Pérez Arias, Simon Friedberger, Uwe D. Hanebeck
Title : Using Extended Range Telepresence to Collect Data of Pedestrian Dynamics
In : Proceedings of the Transportation Research Board 91st Annual Meeting (TRB 2012)
Address : Washington D. C., USA
Date : January 2012
Abstract
In this article a new way to collect data of pedestrian dynamics is
introduced. A virtual reality system consisting of an extended range
telepresence system and a microscopic pedestrian simulation is used
to simplify data collection. The extended range telepresence system
allows a user to move through a virtual environment by natural walking
instead of by using conventional input devices, like a joystick.
The telepresence system is connected to a pedestrian simulation which
produces real time 3D animated output which is presented to the user
with a head-mounted display (HMD) capable of showing 3D imagery.
The simulated pedestrians react to the user of the telepresence as
if it were another simulated pedestrian. With this system data about
pedestrian dynamics can be collected in experiments in which not
all participants need to be real people, but some - ideally all except
for one - can be simulated. This allows the general collection of
data about pedestrian dynamics but also to calibrate model specific
parameters. In this paper three experiments are introduced. However,
the focus of the contribution is to give an idea and an overview
of the combined telepresence-simulation system as data collection
tool.

Publikationen aus dem Jahr 2011

Marcus Baum, Benjamin Noack, Uwe D. Hanebeck,
Random Hypersurface Mixture Models for Tracking Multiple Extended Objects
Proceedings of the 50th IEEE Conference on Decision and Control (CDC 2011), Orlando, Florida, USA, December 2011.
PDF
Author : Marcus Baum, Benjamin Noack, Uwe D. Hanebeck
Title : Random Hypersurface Mixture Models for Tracking Multiple Extended Objects
In : Proceedings of the 50th IEEE Conference on Decision and Control (CDC 2011)
Address : Orlando, Florida, USA
Date : December 2011
Abstract
This paper presents a novel method for tracking multiple extended
objects. The shape of a single extended object is modeled with a
recently developed approach called Random Hypersurface Model (RHM)
that assumes a varying number of measurement sources to lie on scaled
versions of the shape boundaries. This approach is extended by introducing
a so-called Mixture Random Hypersurface Model (Mixture RHM), which
allows for modeling multiple extended targets. Based on this model,
a Gaussian-assumed Bayesian tracking method that provides the means
to track and estimate shapes of multiple extended targets is derived.
Simulations demonstrate the performance of the new approach.
Achim Hekler, Martin Kiefel, Uwe D. Hanebeck,
Stochastic Nonlinear Model Predictive Control with Guaranteed Error Bounds Using Compactly Supported Wavelets
Proceedings of the 50th IEEE Conference on Decision and Control (CDC 2011), Orlando, Florida, USA, December 2011.
PDF
Author : Achim Hekler, Martin Kiefel, Uwe D. Hanebeck
Title : Stochastic Nonlinear Model Predictive Control with Guaranteed Error Bounds Using Compactly Supported Wavelets
In : Proceedings of the 50th IEEE Conference on Decision and Control (CDC 2011)
Address : Orlando, Florida, USA
Date : December 2011
Abstract
In model predictive control, a high quality of control can only be
achieved, if the model of the system reflects the real-world process
as precisely as possible. Therefore, the controller should be capable
of both handling a nonlinear system description and systematically
incorporating uncertainties affecting the system. Since stochastic
nonlinear model predictive control (SNMPC) problems in general cannot
be solved in closed form, either the system model or the occurring
densities have to be approximated. In this paper, we present an SNMPC
framework, which approximates the densities and the reward function
by their wavelet expansions. Due to the few requirements on the shape
and family of the densities or reward function, the presented technique
can be applied to a large class of SNMPC problems. For accelerating
the optimization, we additionally present a novel thresholding technique,
the so-called dynamic thresholding, which neglects coefficients that
are insignificant, while at the same time guaranteeing that the optimal
control input is still chosen. The capabilities of the proposed approach
are demonstrated by simulations with a path planning scenario.
Tobias Kretz, Stefan Hengst, Vidal Roca, Antonia Pérez Arias, Simon Friedberger, Uwe D. Hanebeck,
Calibrating Dynamic Pedestrian Route Choice with an Extended Range Telepresence System
Proceedings of the first IEEE Workshop on Modeling, Simulation and Visual Analysis of Large Crowds in conjunction with the 13th International Conference on Computer Vision (ICCV 2011), Barcelona, Spain, November 2011.
PDF
Author : Tobias Kretz, Stefan Hengst, Vidal Roca, Antonia Pérez Arias, Simon Friedberger, Uwe D. Hanebeck
Title : Calibrating Dynamic Pedestrian Route Choice with an Extended Range Telepresence System
In : Proceedings of the first IEEE Workshop on Modeling, Simulation and Visual Analysis of Large Crowds in conjunction with the 13th International Conference on Computer Vision (ICCV 2011)
Address : Barcelona, Spain
Date : November 2011
Abstract
In this contribution we present the results of a pilot study in which
an Extended Range Telepresence System is used to calibrate parameters
of a pedestrian model for simulation. The parameters control a model
element that is intended to make simulated agents walk in the direction
of the esti- mated smallest remaining travel time. We use this to,
first, show that that an Extended Range Telepresence System can serve
for such a task in general and second to actually find simulation
parameters that yield realistic results.
Lukas Rybok, Simon Friedberger, Uwe D. Hanebeck, Rainer Stiefelhagen,
The KIT Robo-Kitchen Data set for the Evaluation of View-based Activity Recognition Systems
Proceedings of the 2011 11th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2011), Bled, Slovenia, October 2011.
PDF
Author : Lukas Rybok, Simon Friedberger, Uwe D. Hanebeck, Rainer Stiefelhagen
Title : The KIT Robo-Kitchen Data set for the Evaluation of View-based Activity Recognition Systems
In : Proceedings of the 2011 11th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2011)
Address : Bled, Slovenia
Date : October 2011
Abstract
Human action and activity recognition from videos has attracted an
increasing number of researchers in recent years. However, most of
the works aim at multimedia retrieval and surveillance applications,
but rarely at humanoid household robots, even though the robotic
perception of human activities would allow a more natural human-robot
interaction (HRI). To encourage future studies in this domain, we
present in this work a novel data set specifically designed for the
application in HRI scenarios. This Robo-kitchen data set consists
of 14 typical kitchen activities recorded in two different stereo-camera
setups, and each performed by 17 subjects. To establish a baseline
for future work, we extend a state-of-the-art action recognition
method to be applicable on the activity classification problem and
evaluate it on the Robo-kitchen data set showing promising results.
Marco Huber, Peter Krauthausen, Uwe D. Hanebeck,
Superficial Gaussian Mixture Reduction
Proceedings of the IEEE ISIF Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF 2011), Berlin, Germany, October 2011.
PDF
Author : Marco Huber, Peter Krauthausen, Uwe D. Hanebeck
Title : Superficial Gaussian Mixture Reduction
In : Proceedings of the IEEE ISIF Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF 2011)
Address : Berlin, Germany
Date : October 2011
Abstract
Many information fusion tasks involve the processing of Gaussian mixtures
with simple underlying shape, but many components. This paper addresses
the problem of reducing the number of components, allowing for faster
density processing. The proposed approach is based on identifying
components irrelevant for the overall density's shape by means of
the curvature of the density's surface. The key idea is to minimize
an upper bound of the curvature while maintaining a low global reduction
error by optimizing the weights of the original Gaussian mixture
only. The mixture is reduced by assigning zero weights to reducible
components. The main advantages are an alleviation of the model selection
problem, as the number of components is chosen by the algorithm automatically,
the derivation of simple curvature-based penalty terms, and an easy,
efficient implementation. A series of experiments shows the approach
to provide a good trade-off between quality and sparsity.
Marcus Baum, Uwe D. Hanebeck,
Fitting Conics to Noisy Data Using Stochastic Linearization
Proceedings of the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011), San Francisco, California, USA, September 2011.
PDF
Author : Marcus Baum, Uwe D. Hanebeck
Title : Fitting Conics to Noisy Data Using Stochastic Linearization
In : Proceedings of the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011)
Address : San Francisco, California, USA
Date : September 2011
Abstract
Fitting conic sections, e.g., ellipses or circles, to noisy data points
is a fundamental sensor data processing problem, which frequently
arises in robotics. In this paper, we introduce a new procedure for
deriving a recursive Gaussian state estimator for fitting conics
to data corrupted by additive Gaussian noise. For this purpose, the
original exact implicit measurement equation is reformulated with
the help of suitable approximations as an explicit measurement equation
corrupted by multiplicative noise. Based on stochastic linearization,
an efficient Gaussian state estimator is derived for the explicit
measurement equation. The performance of the new approach is evaluated
by means of a typical ellipse fitting scenario.
Dirk Gehrig, Peter Krauthausen, Lukas Rybok, Hildegard Kühne, Tanja Schultz, Uwe D. Hanebeck, Rainer Stiefelhagen,
Combined Intention, Activity, and Motion Recognition for a Humanoid Household Robot
Proceedings of the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011), San Francisco, California, USA, September 2011.
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Author : Dirk Gehrig, Peter Krauthausen, Lukas Rybok, Hildegard Kühne, Tanja Schultz, Uwe D. Hanebeck, Rainer Stiefelhagen
Title : Combined Intention, Activity, and Motion Recognition for a Humanoid Household Robot
In : Proceedings of the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011)
Address : San Francisco, California, USA
Date : September 2011
Abstract
In this paper, a multi-level approach to intention, activity, and
motion recognition for a humanoid robot is proposed. Our system processes
images from a monocular camera and combines this information with
domain knowledge. The recognition works on-line and in real-time,
it is independent of the test person, but limited to predefined view-points.
Main contributions of this paper are the extensible, multi-level
modeling of the robot's vision system, the efficient activity and
motion recognition, and the asynchronous information fusion based
on generic processing of mid-level recognition results. The complementarity
of the activity and motion recognition renders the approach robust
against misclassifications. Experimental results on a real-world
data set of complex kitchen tasks, e.g., Prepare Cereals or Lay Table,
prove the performance and robustness of the multi-level recognition
approach.
Antonia Pérez Arias, Uwe D. Hanebeck,
Motion Control of a Semi-mobile Haptic Interface for Extended Range Telepresence
Proceedings of the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011), San Francisco, California, USA, September 2011.
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Author : Antonia Pérez Arias, Uwe D. Hanebeck
Title : Motion Control of a Semi-mobile Haptic Interface for Extended Range Telepresence
In : Proceedings of the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011)
Address : San Francisco, California, USA
Date : September 2011
Abstract
This paper presents the control concept of a semimobile haptic interface
for extended range telepresence that enables the user to explore
spatially unrestricted target environments even from a small user
environment. The semi-mobile haptic interface consists of a haptic
manipulator mounted on a large grounded Cartesian robot, the prepositioning
unit. The prepositioning unit is controlled in such a way that the
haptic manipulator is kept off its workspace limits. At the same
time, the control algorithm allows the optimal utilization of the
available space in the user environment and guarantees the safety
of the user. The proposed control method is based on the position
and velocity of the end-effector and also takes the position of the
user into account. Moreover, it is robust against noisy measurements
of the user position or outliers due, for example, to occlusions
in the tracking system. Experimental results show the suitability
of the proposed control to provide haptic interaction in extended
range telepresence.
Tobias Kretz, Stefan Hengst, Antonia Pérez Arias, Simon Friedberger, Uwe D. Hanebeck,
Using Extended Range Telepresence to Investigate Route Choice Behavior
Proceedings of the Traffic and Granular Flow Conference 2011 (TGF 2011), Moscow, Russia, September 2011.
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Author : Tobias Kretz, Stefan Hengst, Antonia Pérez Arias, Simon Friedberger, Uwe D. Hanebeck
Title : Using Extended Range Telepresence to Investigate Route Choice Behavior
In : Proceedings of the Traffic and Granular Flow Conference 2011 (TGF 2011)
Address : Moscow, Russia
Date : September 2011
Abstract
A combination of a telepresence system and a microscopic traffic simulator
is introduced. It is evaluated using a hotel evacuation scenario.
Four different kinds of supporting information are compared, standard
exit signs, floor plans with indicated exit routes, guiding lines
on the floor and simulated agents leading the way. The results indicate
that guiding lines are the most efficient way to support an evacuation
but the natural behavior of following others comes very close. On
another level the results are consistent with previously performed
real and virtual experiments and validate the use of a telepresence
system in evacuation studies. It is shown that using a microscopic
traffic simulator extends the possibilities for evaluation, e.g.
by adding simulated humans to the environment.
Marco F. Huber, Frederik Beutler, Uwe D. Hanebeck,
(Semi-)Analytic Gaussian Mixture Filter
Proceedings of the 18th IFAC World Congress (IFAC 2011), Milan, Italy, August 2011.
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Author : Marco F. Huber, Frederik Beutler, Uwe D. Hanebeck
Title : (Semi-)Analytic Gaussian Mixture Filter
In : Proceedings of the 18th IFAC World Congress (IFAC 2011)
Address : Milan, Italy
Date : August 2011
Abstract
In nonlinear filtering, special types of Gaussian mixture filters
are a straightforward extension of Gaussian filters, where linearizing
the system model is performed individually for each Gaussian component.
In this paper, two novel types of linearization are combined with
Gaussian mixture filters. The first linearization is called analytic
stochastic linearization, where the linearization is performed analytically
and exactly, i.e., without Taylor-series expansion or approximate
sample-based density representation. In cases where a full analytical
linearization is not possible, the second approach decomposes the
nonlinear system into a set of nonlinear subsystems that are conditionally
integrable in closed form. These approaches are more accurate than
fully applying classical linearization.
Benjamin Noack, Marcus Baum, Uwe D. Hanebeck,
Automatic Exploitation of Independencies for Covariance Bounding in Fully Decentralized Estimation
Proceedings of the 18th IFAC World Congress (IFAC 2011), Milan, Italy, August 2011.
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Author : Benjamin Noack, Marcus Baum, Uwe D. Hanebeck
Title : Automatic Exploitation of Independencies for Covariance Bounding in Fully Decentralized Estimation
In : Proceedings of the 18th IFAC World Congress (IFAC 2011)
Address : Milan, Italy
Date : August 2011
Abstract
Especially in the field of sensor networks and multi-robot systems,
fully decentralized estimation techniques are of particular interest.
As the required elimination of the complex dependencies between estimates
generally yields inconsistent results, several approaches, e.g.,
covariance intersection, maintain consistency by providing conservative
estimates. Unfortunately, these estimates are often too conservative
and therefore, much less informative than a corresponding centralized
approach. In this paper, we provide a concept that conservatively
decorrelates the estimates while bounding the unknown correlations
as closely as possible. For this purpose, known independent quantities,
such as measurement noise, are explicitly identified and exploited.
Based on tight covariance bounds, the new approach allows for an
intuitive and systematic derivation of appropriate tailor-made filter
equations and does not require heuristics. Its performance is demonstrated
in a comparative study within a typical SLAM scenario.
Marcus Baum, Uwe D. Hanebeck,
Shape Tracking of Extended Objects and Group Targets with Star-Convex RHMs
Proceedings of the 14th International Conference on Information Fusion (Fusion 2011), Chicago, Illinois, USA, July 2011.
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Author : Marcus Baum, Uwe D. Hanebeck
Title : Shape Tracking of Extended Objects and Group Targets with Star-Convex RHMs
In : Proceedings of the 14th International Conference on Information Fusion (Fusion 2011)
Address : Chicago, Illinois, USA
Date : July 2011
Abstract
This paper is about tracking an extended object or a group target,
which gives rise to a varying number of measurements from different
measurement sources. For this purpose, the shape of the target is
tracked in addition to its kinematics. The target extent is modeled
with a new approach called Random Hypersurface Model (RHM) that assumes
varying measurement sources to lie on scaled versions of the shape
boundaries. In this paper, a star-convex RHM is introduced for tracking
star-convex shape approximations of targets. Bayesian inference for
star-convex RHM is performed by means of a Gaussian-assumed state
estimator allowing for an efficient recursive closed-form measurement
update. Simulations demonstrate the performance of this approach
for typical extended object and group tracking scenarios.
Winner Best Student Paper Award Certificate (PDF)
Marcus Baum, Uwe D. Hanebeck,
Using Symmetric State Transformations for Multi-Target Tracking
Proceedings of the 14th International Conference on Information Fusion (Fusion 2011), Chicago, Illinois, USA, July 2011.
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Author : Marcus Baum, Uwe D. Hanebeck
Title : Using Symmetric State Transformations for Multi-Target Tracking
In : Proceedings of the 14th International Conference on Information Fusion (Fusion 2011)
Address : Chicago, Illinois, USA
Date : July 2011
Abstract
This paper is about the use of symmetric state transformations for
multi-target tracking. First, a novel method for obtaining point
estimates for multi-target states is proposed. The basic idea is
to apply a symmetric state transformation to the original state in
order to compute a minimum mean-square-error (MMSE) estimate in a
transformed state. By this means, the known shortcomings of MMSE
estimates for multi-target states can be avoided. Second, a new multi-target
tracking method based on state transformations is suggested, which
entirely performs the time and measurement update in a transformed
space and thus, avoids the explicit calculation of data association
hypotheses and removes the target identity from the estimation problem.
The performance of the new approach is evaluated by means of tracking
two crossing targets.
Marcus Baum, Benjamin Noack, Frederik Beutler, Dominik Itte, Uwe D. Hanebeck,
Optimal Gaussian Filtering for Polynomial Systems Applied to Association-free Multi-Target Tracking
Proceedings of the 14th International Conference on Information Fusion (Fusion 2011), Chicago, Illinois, USA, July 2011.
PDF
Author : Marcus Baum, Benjamin Noack, Frederik Beutler, Dominik Itte, Uwe D. Hanebeck
Title : Optimal Gaussian Filtering for Polynomial Systems Applied to Association-free Multi-Target Tracking
In : Proceedings of the 14th International Conference on Information Fusion (Fusion 2011)
Address : Chicago, Illinois, USA
Date : July 2011
Abstract
This paper is about tracking multiple targets with the so-called Symmetric
Measurement Equation (SME) filter. The SME filter uses symmetric
functions, e.g., symmetric polynomials, in order to remove the data
association uncertainty from the measurement equation. By this means,
the data association problem is converted to a nonlinear state estimation
problem. In this work, an efficient optimal Gaussian filter based
on analytic moment calculation for discrete-time multi-dimensional
polynomial systems corrupted with Gaussian noise is derived, and
then applied to the polynomial system resulting from the SME filter.
The performance of the new method is compared to an UKF implementation
by means of typical multiple target tracking scenarios.
Evgeniya Bogatyrenko, Uwe D. Hanebeck,
Adaptive Model-Based Visual Stabilization of Image Sequences Using Feedback
Proceedings of the 14th International Conference on Information Fusion (Fusion 2011), Chicago, Illinois, USA, July 2011.
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Author : Evgeniya Bogatyrenko, Uwe D. Hanebeck
Title : Adaptive Model-Based Visual Stabilization of Image Sequences Using Feedback
In : Proceedings of the 14th International Conference on Information Fusion (Fusion 2011)
Address : Chicago, Illinois, USA
Date : July 2011
Abstract
Visual stabilization proposed in this paper compensates changes of
the scene caused by motion and deformation of an observed object.
This is of high importance in computer-assisted beating heart surgery,
where the views of the beating heart should be stabilized. The proposed
model-based method defines visual stabilization as a transformation
of the current image sequence to a stabilized image sequence. This
transformation incorporates physical model of the observed object
and model of the measurement process. In contrast to standard approaches,
the quality of the visual stabilization is continuously evaluated
and improved in two aspects. On the one hand, discretization errors
are reduced. On the other hand, the parameters of the underlying
models are adjusted. The performance of the proposed method is evaluated
in an experiment with a pressure-regulated artificial heart. Compared
with standard methods, the model-based method provides higher accuracy,
which is additionally improved by a feedback mechanism.
Peter Krauthausen, Patrick Ruoff, Uwe D. Hanebeck,
Sparse Mixture Conditional Density Estimation by Superficial Regularization
Proceedings of the 14th International Conference on Information Fusion (Fusion 2011), Chicago, Illinois, USA, July 2011.
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Author : Peter Krauthausen, Patrick Ruoff, Uwe D. Hanebeck
Title : Sparse Mixture Conditional Density Estimation by Superficial Regularization
In : Proceedings of the 14th International Conference on Information Fusion (Fusion 2011)
Address : Chicago, Illinois, USA
Date : July 2011
Abstract
In this paper, the estimation of conditional densities between continuous
random variables from noisy samples is considered. The conditional
densities are modeled as heteroscedastic Gaussian mixture densities
allowing for closed-form solution of Bayesian inference with full-densities.
The main contributions of this paper are an improved generalization
quality of the estimates by the introduction of a superficial regularizer,
the consideration of model uncertainty relative to local data densities
by means of adaptive covariances, and the proposition of an efficient
distance-based estimation algorithm. This algorithm corresponds to
an iterative nested optimization scheme, optimizing hyper-parameters,
component placement, and mixture weights. The obtained solutions
are sparse, smooth, and generalize well as benchmark experiments,
e.g., in nonlinear filtering show.
Benjamin Noack, Marcus Baum, Uwe D. Hanebeck,
Covariance Intersection in Nonlinear Estimation Based on Pseudo Gaussian Densities
Proceedings of the 14th International Conference on Information Fusion (Fusion 2011), Chicago, Illinois, USA, July 2011.
PDF
Author : Benjamin Noack, Marcus Baum, Uwe D. Hanebeck
Title : Covariance Intersection in Nonlinear Estimation Based on Pseudo Gaussian Densities
In : Proceedings of the 14th International Conference on Information Fusion (Fusion 2011)
Address : Chicago, Illinois, USA
Date : July 2011
Abstract
Many modern fusion architectures are designed to process and fuse
data in networked systems. Alongside the advantages, such as scalability and
robustness, distributed fusion techniques particularly have to tackle the problem of dependencies
between locally processed data. In linear estimation problems, uncertain
quantities with unknown cross-correlations can be fused by means
of the covariance intersection algorithm, which avoids overconfident
fusion results. However, for nonlinear system dynamics and sensor
models perturbed by arbitrary noise, it is not only a problem to
characterize and parameterize dependencies between estimates, but
also to find a proper notion of consistency. This paper addresses
these issues by transforming the state estimates to a different state
space, where the corresponding densities are Gaussian and only linear
dependencies between estimates, i.e., correlations, can arise. These
pseudo Gaussian densities then allow the notion of covariance consistency
to be used in distributed nonlinear state estimation.
Marc Reinhardt, Benjamin Noack, Marcus Baum, Uwe D. Hanebeck,
Analysis of Set-theoretic and Stochastic Models for Fusion under Unknown Correlations
Proceedings of the 14th International Conference on Information Fusion (Fusion 2011), Chicago, Illinois, USA, July 2011.
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Author : Marc Reinhardt, Benjamin Noack, Marcus Baum, Uwe D. Hanebeck
Title : Analysis of Set-theoretic and Stochastic Models for Fusion under Unknown Correlations
In : Proceedings of the 14th International Conference on Information Fusion (Fusion 2011)
Address : Chicago, Illinois, USA
Date : July 2011
Abstract
In data fusion theory, multiple estimates are combined to yield an
optimal result. In this paper, the set of all possible results is
investigated, when two random variables with unknown correlations
are fused. As a first step, recursive processing of the set of estimates
is examined. Besides set-theoretic considerations, the lack of knowledge
about the unknown correlation coefficient is modeled as a stochastic
quantity. Especially, a uniform model is analyzed, which provides
a new optimization criterion for the covariance intersection algorithm
in scalar state spaces. This approach is also generalized to multi-dimensional
state spaces in an approximative, but fast and scalable way, so that
consistent estimates are obtained.
Patrick Ruoff, Peter Krauthausen, Uwe D. Hanebeck,
Progressive Correction for Deterministic Dirac Mixture Approximations
Proceedings of the 14th International Conference on Information Fusion (Fusion 2011), Chicago, Illinois, USA, July 2011.
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Author : Patrick Ruoff, Peter Krauthausen, Uwe D. Hanebeck
Title : Progressive Correction for Deterministic Dirac Mixture Approximations
In : Proceedings of the 14th International Conference on Information Fusion (Fusion 2011)
Address : Chicago, Illinois, USA
Date : July 2011
Abstract
Since the advent of Monte-Carlo particle filtering, particle representations
of densities have become increasingly popular due to their flexibility
and implicit adaptive resolution. In this paper, an algorithm for
the multiplication of a systematic Dirac mixture (DM) approximation
with a continuous likelihood function is presented, which applies
a progressive correction scheme, in order to avoid the particle degeneration
problem. The preservation of sample regularity and therefore, representation
quality of the underlying smooth density, is ensured by including
a new measure of smoothness for Dirac mixtures, the DM energy, into
the distance measure. A comparison to common correction schemes in
Monte-Carlo methods reveals large improvements especially in cases
of small overlap between the likelihood and prior density, as well
as for multi-modal likelihoods.
Evgeniya Bogatyrenko, Uwe D. Hanebeck,
Visual Stabilization of Beating Heart Motion by Model-based Transformation of Image Sequences
Proceedings of the 2011 American Control Conference (ACC 2011), San Francisco, California, USA, June 2011.
PDF
Author : Evgeniya Bogatyrenko, Uwe D. Hanebeck
Title : Visual Stabilization of Beating Heart Motion by Model-based Transformation of Image Sequences
In : Proceedings of the 2011 American Control Conference (ACC 2011)
Address : San Francisco, California, USA
Date : June 2011
Abstract
In order to assist a surgeon by operating on a beating heart, visual
stabilization makes the beating heart appear still to a surgeon by
providing the current heart view as stationary and non-moving. In
this way, the surgeon is not disturbed during an operation by a motion
of the heart and can get an impression of performing conventional
surgery. In contrast to existing methods for visual stabilization,
the proposed approach involves a model-based transformation of image
sequences provided by a camera system. This transformation incorporates
the knowledge of physical characteristics of the heart in form of
a mathematical model of the heart surface. Its main advantage is
that the uncertainties of the model and measurements are considered.
This occurs by estimating the parameters of the transformation. Furthermore,
the quality of the visual stabilization is additionally improved
by adapting the parameters of the underlying physical model. A performance
of the proposed approach is evaluated in an experiment with a pressure-regulated
artificial heart. In comparison to standard approaches, it provides
superior results illustrating the high quality of the visual stabilization.
Marco F. Huber, Frederik Beutler, Uwe D. Hanebeck,
Semi-Analytic Gaussian Assumed Density Filter
Proceedings of the 2011 American Control Conference (ACC 2011), San Francisco, California, USA, June 2011.
PDF
Author : Marco F. Huber, Frederik Beutler, Uwe D. Hanebeck
Title : Semi-Analytic Gaussian Assumed Density Filter
In : Proceedings of the 2011 American Control Conference (ACC 2011)
Address : San Francisco, California, USA
Date : June 2011
Abstract
For Gaussian Assumed Density Filtering based on moment matching, a
framework for the efficient calculation of posterior moments is proposed
that exploits the structure of the given nonlinear system. The key
idea is a careful discretization of some dimensions of the state
space only in order to decompose the system into a set of nonlinear
subsystems that are conditionally integrable in closed form. This
approach is more efficient than full discretization approaches. In
addition, the new decomposition is far more general than known Rao-Blackwellization
approaches relying on conditionally linear subsystems. As a result,
the new framework is applicable to a much larger class of nonlinear
systems.
Peter Krauthausen, Masoud Roschani, Uwe D. Hanebeck,
Incorporating Prior Knowledge into Nonparametric Conditional Density Estimation
Proceedings of the 2011 American Control Conference (ACC 2011), San Francisco, California, USA, June 2011.
PDF
Author : Peter Krauthausen, Masoud Roschani, Uwe D. Hanebeck
Title : Incorporating Prior Knowledge into Nonparametric Conditional Density Estimation
In : Proceedings of the 2011 American Control Conference (ACC 2011)
Address : San Francisco, California, USA
Date : June 2011
Abstract
In this paper, the problem of sparse nonparametric conditional density
estimation based on samples and prior knowledge is addressed. The
prior knowledge may be restricted to parts of the state space and
given as generative models in form of mean-function constraints or
as probabilistic models in the form of Gaussian mixtures. The key
idea is the introduction of additional constraints and a modified
kernel function into the conditional density estimation problem.
This approach to using prior knowledge is a generic solution applicable
to all nonparametric conditional density estimation approaches phrased
as constrained optimization problems. The quality of the estimates,
their sparseness, and the achievable improvements by using prior
knowledge are shown in experiments for both Support-Vector Machine-based
and integral distance-based conditional density estimation.
Benjamin Noack, Daniel Lyons, Matthias Nagel, Uwe D. Hanebeck,
Nonlinear Information Filtering for Distributed Multisensor Data Fusion
Proceedings of the 2011 American Control Conference (ACC 2011), San Francisco, California, USA, June 2011.
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Author : Benjamin Noack, Daniel Lyons, Matthias Nagel, Uwe D. Hanebeck
Title : Nonlinear Information Filtering for Distributed Multisensor Data Fusion
In : Proceedings of the 2011 American Control Conference (ACC 2011)
Address : San Francisco, California, USA
Date : June 2011
Abstract
The information filter has evolved into a key tool for distributed
and decentralized multisensor estimation and control. Essentially,
it is an algebraical reformulation of the Kalman filter and provides
estimates on the information about an uncertain state rather than
on a state itself. Whereas many practicable Kalman filtering techniques
for nonlinear system and sensor models have been developed, approaches
towards nonlinear information filtering are still scarce and limited.
In order to deal with nonlinear systems and sensors, this paper derives
an approximation technique for arbitrary probability densities that
provides the same distributable fusion structure as the linear information
filter. The presented approach not only constitutes a nonlinear version
of the information filter, but it also points the direction to a
Hilbert space structure on probability densities, whose vector space
operations correspond to the fusion and weighting of information.
Antonia Pérez Arias, Henning P. Eberhardt, Florian Pfaff, Uwe D. Hanebeck,
The Plenhaptic Guidance Function for Intuitive Navigation in Extended Range Telepresence Scenarios
Proceedings of the IEEE World Haptics Conference (WHC 2011), Istanbul, Turkey, June 2011.
PDF
Author : Antonia Pérez Arias, Henning P. Eberhardt, Florian Pfaff, Uwe D. Hanebeck
Title : The Plenhaptic Guidance Function for Intuitive Navigation in Extended Range Telepresence Scenarios
In : Proceedings of the IEEE World Haptics Conference (WHC 2011)
Address : Istanbul, Turkey
Date : June 2011
Abstract
In this work, we propose a plenhaptic guidance function that systematically
describes the haptic information for guiding the user in the target
environment. The plenhaptic guidance function defines the strength
of the guidance at any position in space, at any direction, and at
any time, and takes the geometry of the target environment as well
as all possible goals into account. The plenhaptic guidance function,
which can be rendered as active and passive guidance, is sampled
and displayed to the user through a haptic interface in the user
environment. The benefits of the plenhaptic guidance function for
guiding the user to several simultaneous goals while avoiding the
obstacles in a large target environment are demonstrated in real
experiments.
Jan-P. Calliess, Daniel Lyons, Uwe D. Hanebeck,
Lazy auctions for multi-robot collision avoidance and motion control under uncertainty
Autonomous Robots and Multirobot Systems (ARMS) Workshop at AAMAS 2011, Taipei, Taiwan, May 2011.
PDF
Author : Jan-P. Calliess, Daniel Lyons, Uwe D. Hanebeck
Title : Lazy auctions for multi-robot collision avoidance and motion control under uncertainty
In : Autonomous Robots and Multirobot Systems (ARMS) Workshop at AAMAS 2011
Address : Taipei, Taiwan
Date : May 2011
Abstract
We present an auction-flavored multi-robot planning mechanism where
coordination is to be achieved on the occupation of atomic resources
modeled as binary inter-robot constraints. Introducing virtual obstacles,
we show how this approach can be combined with particlebased obstacle
avoidance methods, offering a decentralized, auction-based alternative
to previously established centralized approaches for multirobot open-loop
control. We illustrate the effectiveness of our new approach by presenting
simulations of typical spatially-continuous multirobot path-planning
problems and derive bounds on the collision probability in the presence
of uncertainty.
Johannes Schmid, Frederik Beutler, Benjamin Noack, Uwe D. Hanebeck, Klaus D. Müller-Glaser,
An Experimental Evaluation of Position Estimation Methods for Person Localization in Wireless Sensor Networks
Proceedings of the 8th European Conference on Wireless Sensor Networks (EWSN 2011), 6567:147-162, Bonn, Germany, February 2011.
PDF URL
Author : Johannes Schmid, Frederik Beutler, Benjamin Noack, Uwe D. Hanebeck, Klaus D. Müller-Glaser
Title : An Experimental Evaluation of Position Estimation Methods for Person Localization in Wireless Sensor Networks
In : Proceedings of the 8th European Conference on Wireless Sensor Networks (EWSN 2011)
Address : Bonn, Germany
Date : February 2011
Abstract
In this paper, the localization of persons by means of a Wireless
Sensor Network (WSN) is considered. Persons carry on-body sensor
nodes and move within a WSN. The location of each person is calculated
on this node and communicated through the network to a central data
sink for visualization. Applications of such a system could be found in
mass casualty events, firefighter scenarios, hospitals or retirement homes for example.
For the location estimation on the sensor node, three derivatives of the
Kalman Filter and a closed-form solution (CFS) are applied, compared,
and evaluated in a real-world scenario. A prototype 65-node ZigBee WSN
is implemented and data are collected in in- and outdoor environments
with differently positioned on-body nodes. The described estimators are
then evaluated off-line on the experimentally collected data.
The goal of this paper is to present a comprehensive real-world evaluation of methods for
person localization in a WSN based on received signal strength (RSS) range measurements.
It is concluded that person localization in in- and outdoor environments is possible
under the considered conditions with the considered filters. The compared methods
allow for suffciently accurate localization results and are robust against
inaccurate range measurements.

Publikationen aus dem Jahr 2010

Achim Hekler, Martin Kiefel, Uwe D. Hanebeck,
Nonlinear Bayesian Estimation with Compactly Supported Wavelets
Proceedings of the 2010 IEEE Conference on Decision and Control (CDC 2010), Atlanta, Georgia, USA, December 2010.
PDF
Author : Achim Hekler, Martin Kiefel, Uwe D. Hanebeck
Title : Nonlinear Bayesian Estimation with Compactly Supported Wavelets
In : Proceedings of the 2010 IEEE Conference on Decision and Control (CDC 2010)
Address : Atlanta, Georgia, USA
Date : December 2010
Abstract
Bayesian estimation for nonlinear systems is still a challenging problem, as
in general the type of the true probability density changes and the
complexity increases over time. Hence, approximations of the occurring
equations and/or of the underlying probability density functions are
inevitable. In this paper, we propose an approximation of the conditional
densities by wavelet expansions. This kind of representation allows a sparse
set of characterizing coefficients, especially for smooth or piecewise
smooth density functions. Besides its good approximation properties, fast
algorithms operating on sparse vectors are applicable and thus, a good
trade-off between approximation quality and run-time can be achieved.
Moreover, due to its highly generic nature, it can be applied to a large
class of nonlinear systems with a high modeling accuracy. In particular, the
noise acting upon the system can be modeled by an arbitrary probability
distribution and can influence the system in any way.
Benjamin Noack, Vesa Klumpp, Daniel Lyons, Uwe D. Hanebeck,
Modellierung von Unsicherheiten und Zustandsschätzung mit Mengen von Wahrscheinlichkeitsdichten
tm - Technisches Messen, Oldenbourg Verlag, 77(10):544-550, October 2010.
PDF URL
Author : Benjamin Noack, Vesa Klumpp, Daniel Lyons, Uwe D. Hanebeck
Title : Modellierung von Unsicherheiten und Zustandsschätzung mit Mengen von Wahrscheinlichkeitsdichten
In : tm - Technisches Messen, Oldenbourg Verlag
Address :
Date : October 2010
Abstract
Die systematische Behandlung von Unsicherheiten stellt eine wesentliche
Herausforderung in der Informationsfusion dar. Einerseits müssen
geeignete Darstellungsformen für die Unsicherheiten bestimmt
werden und andererseits darauf aufbauend effiziente Schätzverfahren
hergeleitet werden. Im Allgemeinen wird zwischen stochastischen und
mengenbasierten Unsicherheitsbeschreibungen unterschieden. Dieser
Beitrag stellt ein Verfahren zur Zustandsschätzung vor, welches
simultan stochastische und mengenbasierte Fehlergrößen berücksichtigen
kann, indem unsichere Größen nicht mehr durch eine einzelne
Wahrscheinlichkeitsdichte, sondern durch eine Menge von Dichten repräsentiert
werden. Besonderes Augenmerk liegt hier auf den Vorteilen und Anwendungsmöglichkeiten
dieser Unsicherheitsbeschreibung.
Achim Kuwertz, Marco F. Huber, Felix Sawo, Uwe D. Hanebeck,
Modellbasierte Quellenverfolgung in räumlich ausgedehnten Phänomenen mittels Sensoreinsatzplanung
tm - Technisches Messen, Oldenbourg Verlag, 77(10):551-557, October 2010.
PDF URL
Author : Achim Kuwertz, Marco F. Huber, Felix Sawo, Uwe D. Hanebeck
Title : Modellbasierte Quellenverfolgung in räumlich ausgedehnten Phänomenen mittels Sensoreinsatzplanung
In : tm - Technisches Messen, Oldenbourg Verlag
Address :
Date : October 2010
Abstract
Bewegte Quellen können durch Emission räumlich
ausgedehnte Phänomene wie beispielsweise Schadstoff- oder
Temperaturverteilungen erzeugen. Zur Lokalisierung von Quellen
mit unbekannter Position stehen in vielen Aufgabenstellungen
Informationen nur indirekt durch die verteilte Vermessung des
induzierten Phänomens zur Verfügung - etwa unter Verwendung
stationärer oder mobiler Sensoren. Dieser Beitrag stellt
modellbasierte Verfahren für eine echtzeitfähige Lokalisierung
und Verfolgung von bewegten Quellen vor. Zur gezielten Maximierung
des Informationsgehalts der Messungen wird dabei eine vorausschauende
Sensoreinsatzplanung genutzt, welche eine hohe Lokalisierungsgüte bei
geringem Aufwand ermöglicht.
Frederik Beutler, Marco F. Huber, Uwe D. Hanebeck,
Optimal Stochastic Linearization for Range-based Localization
Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010), Taipei, Taiwan, October 2010.
PDF
Author : Frederik Beutler, Marco F. Huber, Uwe D. Hanebeck
Title : Optimal Stochastic Linearization for Range-based Localization
In : Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010)
Address : Taipei, Taiwan
Date : October 2010
Abstract
In range-based localization, the trajectory of a
mobile object is estimated based on noisy range measurements
between the object and known landmarks. In order to deal
with this uncertain information, a Bayesian state estimator
is presented, which exploits optimal stochastic linearization.
Compared to standard state estimators like the Extended
or Unscented Kalman Filter, where a point-based Gaussian
approximation is used, the proposed approach considers the
entire Gaussian density for linearization. By employing the common
assumption that the state and measurements are jointly
Gaussian, the linearization can be calculated in closed form
and thus analytic expressions for the range-based localization
problem can be derived.
Evgeniya Bogatyrenko, Benjamin Noack, Uwe D. Hanebeck,
Reliable Estimation of Heart Surface Motion under Stochastic and Unknown but Bounded Systematic Uncertainties
Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010), Taipei, Taiwan, October 2010.
PDF
Author : Evgeniya Bogatyrenko, Benjamin Noack, Uwe D. Hanebeck
Title : Reliable Estimation of Heart Surface Motion under Stochastic and Unknown but Bounded Systematic Uncertainties
In : Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010)
Address : Taipei, Taiwan
Date : October 2010
Abstract
A reliable estimation of heart surface motion
is an important prerequisite for the synchronization of surgical
instruments in robotic beating heart surgery. In general, only
an imprecise description of the heart dynamics and measurement
systems is available. This means that the estimation of heart
motion is corrupted by stochastic and systematic uncertainties.
Without consideration of these uncertainties, the obtained results
will be inaccurate and a safe robotic operation cannot be guaranteed.
Until now, existing approaches for estimating the motion of the
heart surface are either deterministic or treat only stochastic
uncertainties. The proposed method extends the heart motion
estimation to the simultaneous consideration of stochastic and
unknown but bounded systematic uncertainties. It computes dynamic
bounds in order to provide the surgeon with a guidance by
constraining the motion of the surgical instruments and thereby
protecting sensitive tissue.
Ferdinand Packi, Antonia Pérez Arias, Frederik Beutler, Uwe D. Hanebeck,
A Wearable System for the Wireless Experience of Extended Range Telepresence
Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010), Taipei, Taiwan, October 2010.
PDF
Author : Ferdinand Packi, Antonia Pérez Arias, Frederik Beutler, Uwe D. Hanebeck
Title : A Wearable System for the Wireless Experience of Extended Range Telepresence
In : Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010)
Address : Taipei, Taiwan
Date : October 2010
Abstract
Extended range telepresence aims at enabling a
user to experience virtual or remote environments, taking his
own body movements as an input to define walking speed and
viewing direction. Therefore, localization and tracking of the
users pose (position and orientation) is necessary to perform
a body-centered scene rendering. Visual and acoustic feedback
is provided to the user by a head mounted display (HMD).
To allow for free movement within the user environment, the
tracking system is supposed to be user-wearable and entirely
wireless. Consequently, a lightweight design is presented fea-
turing small dimensions to fit into a conventional 13"laptop
backpack, which satisfies the above stated demands for highly
immersive extended range telepresence scenarios. Dedicated
embedded hardware combined with off-the-shelf components
is employed to form a robust, low-cost telepresence system that
can be easily installed in any living room.
Antonia Pérez Arias, Uwe D. Hanebeck,
Wide-Area Haptic Guidance: Taking the User by the Hand
Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010), Taipei, Taiwan, October 2010.
PDF
Author : Antonia Pérez Arias, Uwe D. Hanebeck
Title : Wide-Area Haptic Guidance: Taking the User by the Hand
In : Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010)
Address : Taipei, Taiwan
Date : October 2010
Abstract
In this paper, we present a novel use of haptic
information in extended range telepresence, the wide-area haptic
guidance. It consists of force and position signals applied to the
user's hand in order to improve safety, accuracy, and speed in
some telepresent tasks. Wide-area haptic guidance assists the
user in reaching a desired position in a remote environment of
arbitrary size without degrading the feeling of presence. Several
methods for haptic guidance are analyzed. With active haptic
guidance, the user is guided by superimposed forces that pull
him into the desired direction of motion, whereas under passive
haptic guidance, the movement of the user is lightened in the
preferred direction and constrained in the other directions. By
using closed-loop haptic guidance instead of open-loop haptic
guidance, not only is the user guided to his target but also
the deviation from the desired target path is reduced. The
proposed guidance methods were tested with a haptic interface
specifically designed for extended range telepresence.
Peter Krauthausen, Uwe D. Hanebeck,
A Model-Predictive Switching Approach To Efficient Intention Recognition
Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010), Taipei, Taiwan, October 2010.
PDF
Author : Peter Krauthausen, Uwe D. Hanebeck
Title : A Model-Predictive Switching Approach To Efficient Intention Recognition
In : Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010)
Address : Taipei, Taiwan
Date : October 2010
Abstract
Estimating a user's intention is central to close
human-robot cooperation. In this paper, the problem of per-
forming intention recognition with tree-structured Dynamic
Bayesian Networks for large environments with many features
is addressed. The proposed approach reduces the computational
complexity of inference O(b^s) for tree-structured measurement
models with an average branching factor b and tree height s
to O((b)s), where b << b. The key idea is to switch between a
finite set of reduced system and measurement models in order
to restrict inference to the most important features. A model
predictive approach to online switching between the reduced
models is proposed that exploits an upper bound of the distances
of the reduced models to the full model. The effectiveness of
the proposed algorithm is validated in the intention recognition
for a humanoid robot using a telepresent household scenario.
Marcus Baum, Michael Feldmann, Dietrich Fränken, Uwe D. Hanebeck, Wolfgang Koch,
Extended Object and Group Tracking: A Comparison of Random Matrices and Random Hypersurface Models
Proceedings of the IEEE ISIF Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF 2010), Leipzig, Germany, October 2010.
PDF
Author : Marcus Baum, Michael Feldmann, Dietrich Fränken, Uwe D. Hanebeck, Wolfgang Koch
Title : Extended Object and Group Tracking: A Comparison of Random Matrices and Random Hypersurface Models
In : Proceedings of the IEEE ISIF Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF 2010)
Address : Leipzig, Germany
Date : October 2010
Abstract
Based on previous work of the authors, this paper provides a comparison
of two different tracking methodologies for extended objects and
group targets, where the true shape of the extent is approximated by
an ellipsoid. Although both methods exploit usual sensor data, i.e.,
position measurements of varying scattering centers, the distinctions
are a consequence of the different modeling of the extent as a symmetric
positive definite random matrix on the one hand and an elliptic random
hypersurface model on the other. Besides analyzing the fundamental
assumptions and a comparison of the properties of these tracking methods,
simulation results are presented based on a static tracking environment
to highlight especially the differences in the update step for the
extension estimate.
Evgeniya Bogatyrenko, Pascal Pompey, Uwe D. Hanebeck,
Efficient Physics-Based Tracking of Heart Surface Motion for Beating Heart Surgery Robotic Systems
International Journal of Computer Assisted Radiology and Surgery (IJCARS 2010), 6(3):387-399, August 2010.
PDF URL
Author : Evgeniya Bogatyrenko, Pascal Pompey, Uwe D. Hanebeck
Title : Efficient Physics-Based Tracking of Heart Surface Motion for Beating Heart Surgery Robotic Systems
In : International Journal of Computer Assisted Radiology and Surgery (IJCARS 2010)
Address :
Date : August 2010
Abstract
Purpose: Tracking of beating heart motion in a robotic
surgery system is required for complex cardiovascular interventions.
Methods: A heart surface motion tracking method is developed,
including a stochastic physics-based heart surface
model and an efficient reconstruction algorithm. The algorithm
uses the constraints provided by the model that exploits
the physical characteristics of the heart. The main advantage
of the model is that it is more realistic than most standard
heartmodels. Additionally, no explicit matching between the
measurements and the model is required. The application of
meshless methods significantly reduces the complexity of
physics-based tracking.
Results: Based on the stochastic physical model of the heart
surface, this approach considers the motion of the intervention
area and is robust to occlusions and reflections. The
tracking algorithm is evaluated in simulations and experiments
on an artificial heart. Providing higher accuracy than
the standardmodel-based methods, it successfully copes with
occlusions and provides high performance even when all
measurements are not available.
Conclusions: Combining the physical and stochastic description
of the heart surface motion ensures physically correct
and accurate prediction. Automatic initialization of the physics-based
cardiac motion tracking enables system evaluation
in a clinical environment.
Peter Krauthausen, Uwe D. Hanebeck,
Situation-Specific Intention Recognition for Human-Robot-Cooperation
33rd Annual German Conference on Artificial Intelligence (KI 2010), Karlsruhe, Germany, September 2010.
PDF
Author : Peter Krauthausen, Uwe D. Hanebeck
Title : Situation-Specific Intention Recognition for Human-Robot-Cooperation
In : 33rd Annual German Conference on Artificial Intelligence (KI 2010)
Address : Karlsruhe, Germany
Date : September 2010
Abstract
Recognizing human intentions is part of the decision
process in many technical devices. In order to achieve
natural interaction, the required estimation quality and
the used computation time need to be balanced. This becomes
challenging, if the number of sensors is high and measurement
systems are complex. In this paper, a model predictive approach
to this problem based on online switching of small,
situation-specific Dynamic Bayesian Networks is proposed.
The contributions are an efficient modeling and inference
of situations and a greedy model predictive switching algorithm
maximizing the mutual information of predicted situations. The
achievable accuracy and computational savings are demonstrated
for a household scenario by using an extended range telepresence system.
Ferdinand Packi, Frederik Beutler, Uwe D. Hanebeck,
Wireless Acoustic Tracking for Extended Range Telepresence
Proceedings of the 2010 IEEE International Conference on Indoor Positioning and Indoor Navigation (IPIN 2010), Zürich, Switzerland, September 2010.
PDF
Author : Ferdinand Packi, Frederik Beutler, Uwe D. Hanebeck
Title : Wireless Acoustic Tracking for Extended Range Telepresence
In : Proceedings of the 2010 IEEE International Conference on Indoor Positioning and Indoor Navigation (IPIN 2010)
Address : Zürich, Switzerland
Date : September 2010
Abstract
Telepresence systems enable a user to experience
virtual or distant environments by providing sensory feedback.
Appropriate devices include head mounted displays (HMD) for
visual perception, headphones for auditory response, or even
haptic displays for tactile sensation and force feedback. While
most common designs use dedicated input devices like joysticks
or a space mouse, the approach followed in the present work
takes the user's position and viewing direction as an input, as he
walks freely in his local surroundings. This is achieved by using
acoustic tracking, where the user's pose (position and orientation)
is estimated on the basis of ranges measured between a set
of wall-fastened loudspeakers and a microphone array fixed on
the user's HMD. To allow for natural user motion, a wearable,
fully wireless telepresence system is introduced. The increase in
comfort compared to wired solutions is obvious, as the user's
awareness of distracting cables is taken away during walking.
Also the lightweight design and small dimensions contribute to
ergonomics, as the whole assembly fits well into a small backpack.
Ioana Gheta, Marcus Baum, Andrey Belkin, Jürgen Beyerer, Uwe D. Hanebeck,
Three Pillar Information Management System for Modeling the Environment of Autonomous Systems
Proceedings of the 2010 IEEE International Conference on Virtual Environments, Human-Computer Interfaces and Measurement Systems (VECIMS 2010), Taranto, Italy, September 2010.
PDF
Author : Ioana Gheta, Marcus Baum, Andrey Belkin, Jürgen Beyerer, Uwe D. Hanebeck
Title : Three Pillar Information Management System for Modeling the Environment of Autonomous Systems
In : Proceedings of the 2010 IEEE International Conference on Virtual Environments, Human-Computer Interfaces and Measurement Systems (VECIMS 2010)
Address : Taranto, Italy
Date : September 2010
Abstract
This contribution is about an information management and storage system for modeling the
environment of autonomous systems. The three pillars of the system consist of prior
knowledge, environment model and sensory information. The main pillar is the environment model,
which supplies the autonomous system with relevant information about its current environment.
For this purpose, an abstract representation of the real world is created, where instances
with attributes and relations serve as virtual substitutes of entities (persons and objects)
of the real world. The environment model is created based on sensory information about
the real world. The gathered sensory information is typically uncertain in a stochastic
sense and is represented in the environment model by means of Degree-of-Belief (DoB) distributions.
The prior knowledge contains all relevant background knowledge (e.g., concepts organized in ontologies)
for creating and maintaining the environment model. The concept of the three pillar information system
has previously been published. Therefore this contribution focuses on further central properties
of the system. Furthermore, the development status and possible applications as well as evaluation
scenarios are discussed.
Marcus Baum, Uwe D. Hanebeck,
Association-free Tracking of Two Closely Spaced Targets
Proceedings of the 2010 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2010), Salt Lake City, Utah, USA, September 2010.
PDF
Author : Marcus Baum, Uwe D. Hanebeck
Title : Association-free Tracking of Two Closely Spaced Targets
In : Proceedings of the 2010 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2010)
Address : Salt Lake City, Utah, USA
Date : September 2010
Abstract
This paper introduces a new concept for tracking closely spaced targets in Cartesian
space based on position measurements corrupted with additive Gaussian noise.
The basic idea is to select a special state representation that eliminates the target identity
and avoids the explicit evaluation of association probabilities.
One major advantage of this approach is that the resulting likelihood function for this special problem is unimodal.
Hence, it is especially suitable for closely spaced targets.
The resulting estimation problem can be tackled with a standard nonlinear estimator.
In this work, we focus on two targets in two-dimensional Cartesian space.
The Cartesian coordinates of the targets are represented by means of extreme values, i.e.,
minima and maxima. Simulation results demonstrate the feasibility of the new approach.
Marcus Baum, Uwe D. Hanebeck,
Tracking a Minimum Bounding Rectangle based on Extreme Value Theory
Proceedings of the 2010 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2010), Salt Lake City, Utah, USA, September 2010.
PDF
Author : Marcus Baum, Uwe D. Hanebeck
Title : Tracking a Minimum Bounding Rectangle based on Extreme Value Theory
In : Proceedings of the 2010 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2010)
Address : Salt Lake City, Utah, USA
Date : September 2010
Abstract
In this paper, a novel Bayesian estimator for the minimum bounding axis-aligned rectangle of a point set based on noisy measurements is
derived. Each given measurement stems from an unknown point and is corrupted with additive Gaussian noise.
Extreme value theory is applied in order to derive a linear measurement equation for the problem.
The new estimator is applied to the problem of group target and extended object tracking.
Instead of estimating each single group member or point feature explicitly, the basic idea is to track a summarizing shape, namely the minimum bounding rectangle, of the
group. Simulation results demonstrate the feasibility of the estimator.
Marcus Baum, Ioana Gheta, Andrey Belkin, Jürgen Beyerer, Uwe D. Hanebeck,
Data Association in a World Model for Autonomous Systems
Proceedings of the 2010 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2010), Salt Lake City, Utah, USA, September 2010.
PDF
Author : Marcus Baum, Ioana Gheta, Andrey Belkin, Jürgen Beyerer, Uwe D. Hanebeck
Title : Data Association in a World Model for Autonomous Systems
In : Proceedings of the 2010 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2010)
Address : Salt Lake City, Utah, USA
Date : September 2010
Abstract
This contribution introduces a three pillar information storage and management
system for modeling the environment of autonomous systems. The main
characteristics is the separation of prior knowledge, environment model and
sensor information. In the center of the system is the environment model, which
provides the autonomous system with information about the current state of the
environment. It consists of instances with attributes and relations as virtual
substitutes of entities (persons and objects) of the real world.
Important features are the representation of uncertain information by means
of Degree-of-Belief (DoB) distributions,
the information exchange between the three pillars as well as creation,
deletion and update of instances, attributes and relations in the environment
model. In this work, a Bayesian method for fusing new observations to the
environment model is introduced. For this purpose, a Bayesian data association
method is derived. The main question answered here is the
observation-to-instance mapping
and the decision mechanisms for creating a new instance or
updating already existing instances in the environment model.
Frederik Beutler, Marco F. Huber, Uwe D. Hanebeck,
Semi-Analytic Stochastic Linearization for Range-Based Pose Tracking
Proceedings of the 2010 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2010), Salt Lake City, Utah, USA, September 2010.
PDF
Author : Frederik Beutler, Marco F. Huber, Uwe D. Hanebeck
Title : Semi-Analytic Stochastic Linearization for Range-Based Pose Tracking
In : Proceedings of the 2010 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2010)
Address : Salt Lake City, Utah, USA
Date : September 2010
Abstract
In range-based pose tracking, the translation and
rotation of an object with respect to a global coordinate system
has to be estimated. The ranges are measured between the
target and the global frame. In this paper, an intelligent decomposition
is introduced in order to reduce the computational
effort for pose tracking. Usually, decomposition procedures only
exploit conditionally linear models. In this paper, this principle
is generalized to conditionally integrable substructures and
applied to pose tracking. Due to a modified measurement
equation, parts of the problem can even be solved analytically.
Evgeniya Bogatyrenko, Uwe D. Hanebeck,
Simultaneous State and Parameter Estimation for Physics-Based Tracking of Heart Surface Motion
Proceedings of the 2010 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2010), Salt Lake City, Utah, USA, September 2010.
PDF
Author : Evgeniya Bogatyrenko, Uwe D. Hanebeck
Title : Simultaneous State and Parameter Estimation for Physics-Based Tracking of Heart Surface Motion
In : Proceedings of the 2010 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2010)
Address : Salt Lake City, Utah, USA
Date : September 2010
Abstract
Most existing approaches for tracking of the
beating heart motion assume known cardiac kinematics and
material parameters. However, these assumptions are not realistic
for application in beating heart surgery. In this paper,
a novel probabilistic tracking approach based on a physical
model of the heart surface is presented. In contrast to existing
approaches, the physical information about heart kinematics
and material properties is incorporated and considered in
an estimation of the heart behavior. An additional advantage
is that the time-dependencies and uncertainties of the heart
parameters are efficiently handled by exploiting simultaneous
state and parameter estimation. Furthermore, by decomposing
the state into linear and nonlinear substructures, the computational
complexity of the estimation problem is reduced. The
experimental results demonstrate the high performance of the
method proposed in this paper. The solution of the parameter
identification problem allows a personalized physical model and
opens up possibilities to apply the physics-based tracking of the
heart surface motion in a clinical environment.
Peter Krauthausen, Henning Eberhardt, Uwe D. Hanebeck,
Multivariate Parametric Density Estimation Based On The Modified Cramér-von Mises Distance
Proceedings of the 2010 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2010), Salt Lake City, Utah, USA, September 2010.
PDF
Author : Peter Krauthausen, Henning Eberhardt, Uwe D. Hanebeck
Title : Multivariate Parametric Density Estimation Based On The Modified Cramér-von Mises Distance
In : Proceedings of the 2010 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2010)
Address : Salt Lake City, Utah, USA
Date : September 2010
Abstract
In this paper, a novel distance-based density
estimation method is proposed, which considers the overall
density function in the goodness-of-fit. In detail, the parameters
of Gaussian mixture densities are estimated from samples,
based on the distance of the cumulative distributions over
the entire state space. Due to the ambiguous definition of the
standard multivariate cumulative distribution, the Localized
Cumulative Distribution and a modified Cram\\\\ér-von Mises
distance measure are employed. A further contribution is the
derivation of a simple-to-implement optimization procedure
for the optimization problem. The proposed approach's good
performance in estimating arbitrary Gaussian mixture densities
is shown in an experimental comparison to the Expectation
Maximization algorithm for Gaussian mixture densities.
Peter Krauthausen, Uwe D. Hanebeck,
Regularized Non-Parametric Multivariate Density and Conditional Density Estimation
Proceedings of the 2010 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2010), Salt Lake City, Utah, USA, September 2010.
PDF
Author : Peter Krauthausen, Uwe D. Hanebeck
Title : Regularized Non-Parametric Multivariate Density and Conditional Density Estimation
In : Proceedings of the 2010 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2010)
Address : Salt Lake City, Utah, USA
Date : September 2010
Abstract
In this paper, a distance-based method for both
multivariate non-parametric density and conditional density
estimation is proposed. The contributions are the formulation
of both density estimation problems as weight optimization
problems for Gaussian mixtures centered about samples with
identical parameters. Furthermore, the minimization is based
on the modified Cram\\\\ér-von Mises distance of the Localized
Cumulative Distributions, removing the ambiguity of the defi-
nition of the multivariate cumulative distribution function. The
minimization problem is amended with a regularization term
penalizing the densities' roughness to avoid overfitting. The
resulting estimation problems for both densities and conditional
densities are shown to be phrasable in the form of readily
implementable quadratic programs. Experimental comparison
against EM, SVR, and GPR based on the log-likelihood and
performance in benchmark recursive filtering applications show
high quality of the densities and good performance at less
computational cost, i.e., the density representations are sparser.
Daniel Lyons, Achim Hekler, Markus Kuderer, Uwe D. Hanebeck,
Robust Model Predictive Control with Least Favorable Measurements
Proceedings of the 2010 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2010), Salt Lake City, Utah, USA, September 2010.
PDF
Author : Daniel Lyons, Achim Hekler, Markus Kuderer, Uwe D. Hanebeck
Title : Robust Model Predictive Control with Least Favorable Measurements
In : Proceedings of the 2010 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2010)
Address : Salt Lake City, Utah, USA
Date : September 2010
Abstract
Closed-loop model predictive control of nonlinear systems,
whose internal states are not completely accessible, incorporates
the impact of possible future measurements into the planning
process. When planning ahead in time, those measurements
are not known, so the closed-loop controller accounts for
the expected impact of all potential measurements. We propose a novel
conservative closed-loop control approach that does not calculate the
expected impact of all measurements, but solely considers the single
future measurement that has the worst impact on the control objective.
In doing so, the model predictive controller guarantees robustness
even in the face of high disturbances acting upon the system. Moreover,
by considering only a single dedicated measurement, the complexity of
closed-loop control is reduced significantly. The capabilities of our
approach are evaluated by means of a path planning problem for a mobile robot.
Nominee Best Paper Award
Achim Hekler, Daniel Lyons, Benjamin Noack, Uwe D. Hanebeck,
Nonlinear Model Predictive Control Considering Stochastic and Systematic Uncertainties with Sets of Densities
Proceedings of the IEEE Multi-Conference on Systems and Control (MSC 2010), Yokohama, Japan, September 2010.
PDF
Author : Achim Hekler, Daniel Lyons, Benjamin Noack, Uwe D. Hanebeck
Title : Nonlinear Model Predictive Control Considering Stochastic and Systematic Uncertainties with Sets of Densities
In : Proceedings of the IEEE Multi-Conference on Systems and Control (MSC 2010)
Address : Yokohama, Japan
Date : September 2010
Abstract
In Model Predictive Control, the quality of control
is highly dependent upon the model of the system under control.
Therefore, a precise deterministic model is desirable. However,
in real-world applications, modeling accuracy is typically limited
and systems are generally affected by disturbances. Hence,
it is important to systematically consider these uncertainties
and to model them correctly. In this paper, we present a
novel Nonlinear Model Predictive Control method for systems
affected by two different types of perturbations that are
modeled as being either stochastic or unknown but bounded
quantities. We derive a formal generalization of the Nonlinear
Model Predictive Control principle for considering both types
of uncertainties simultaneously, which is achieved by using
sets of probability densities. In doing so, a more robust and
reliable control is obtained. The capabilities and benefits of
our approach are demonstrated in real-world experiments with
miniature walking robots.
Marcus Baum, Vesa Klumpp, Uwe D. Hanebeck,
A Novel Bayesian Method for Fitting a Circle to Noisy Points
Proceedings of the 13th International Conference on Information Fusion (Fusion 2010), Edinburgh, United Kingdom, July 2010.
PDF
Author : Marcus Baum, Vesa Klumpp, Uwe D. Hanebeck
Title : A Novel Bayesian Method for Fitting a Circle to Noisy Points
In : Proceedings of the 13th International Conference on Information Fusion (Fusion 2010)
Address : Edinburgh, United Kingdom
Date : July 2010
Abstract
This paper introduces a novel recursive Bayesian
estimator for the center and radius of a circle based on
noisy points. Each given point is assumed to be a noisy measurement
of an unknown true point on the circle that is corrupted with known
isotropic Gaussian noise. In contrast to existing approaches, the
novel method does not make assumptions about the true points on
the circle, where the measurements stem from. Closed-form expressions
for the measurement update step are derived. Simulations show that
the novel method outperforms standard Bayesian approaches for
circle fitting.
Marcus Baum, Benjamin Noack, Uwe D. Hanebeck,
Extended Object and Group Tracking with Elliptic Random Hypersurface Models
Proceedings of the 13th International Conference on Information Fusion (Fusion 2010), Edinburgh, United Kingdom, July 2010.
PDF
Author : Marcus Baum, Benjamin Noack, Uwe D. Hanebeck
Title : Extended Object and Group Tracking with Elliptic Random Hypersurface Models
In : Proceedings of the 13th International Conference on Information Fusion (Fusion 2010)
Address : Edinburgh, United Kingdom
Date : July 2010
Abstract
This paper provides new results and
insights for tracking an extended target object
modeled with an Elliptic Random Hypersurface Model (RHM).
An Elliptic RHM specifies the relative squared Mahalanobis
distance of a measurement source to the center of the
target object by means of a one-dimensional random scaling
factor. It is shown that uniformly distributed measurement
sources on an ellipse lead to a uniformly distributed
squared scaling factor. Furthermore, a Bayesian inference
mechanisms tailored to elliptic shapes is introduced, which
is also suitable for scenarios with high measurement noise.
Closed-form expressions for the measurement update in case
of Gaussian and uniformly distributed squared scaling factors are derived.
Frederik Beutler, Uwe D. Hanebeck,
A Two-Step Approach for Offset and Position Estimation from Pseudo-Ranges Applied to Multilateration Tracking
Proceedings of the 13th International Conference on Information Fusion (Fusion 2010), Edinburgh, United Kingdom, July 2010.
PDF
Author : Frederik Beutler, Uwe D. Hanebeck
Title : A Two-Step Approach for Offset and Position Estimation from Pseudo-Ranges Applied to Multilateration Tracking
In : Proceedings of the 13th International Conference on Information Fusion (Fusion 2010)
Address : Edinburgh, United Kingdom
Date : July 2010
Abstract
In multilateration tracking, an object, e.g., an airplane, emits a known
reference signal, which is received by several base stations (sensors) located at
known positions. The receiving times of the signal at the sensors correspond to the times of
arrival (TOA) plus an unknown offset, because the emission time is unknown.
Usually, for estimating the position of the object, the receiving times are
converted to a larger number of time differences of arrival (TDOA) in order
to eliminate the unknown offset. To avoid this conversion, the proposed
approach directly uses the receiving times. This is achieved by 1. determining the optimal offset from the redundant measurements in closed
form and 2. by considering a modified measurement equation. As a result,
position estimation can be performed by optimal stochastic linearization.
Patrick Dunau, Ferdinand Packi, Frederik Beutler, Uwe D. Hanebeck,
Efficient Multilateration Tracking with Concurrent Offset Estimation using Stochastic Filtering Techniques
Proceedings of the 13th International Conference on Information Fusion (Fusion 2010), Edinburgh, United Kingdom, July 2010.
PDF
Author : Patrick Dunau, Ferdinand Packi, Frederik Beutler, Uwe D. Hanebeck
Title : Efficient Multilateration Tracking with Concurrent Offset Estimation using Stochastic Filtering Techniques
In : Proceedings of the 13th International Conference on Information Fusion (Fusion 2010)
Address : Edinburgh, United Kingdom
Date : July 2010
Abstract
Multilateration systems operate by deter-
mining distances between a signal transmitter and a
number of receivers. In aerial surveillance, radio sig-
nals are emitted as Secondary Surveillance Radar (SSR)
by the aircraft, representing the signal transmitter. A
number of base stations (sensors) receive the signals at
different times. Most common approaches use time dif-
ference of arrival (TDOA) measurements, calculated by
subtracting receiving times of one receiver from another.
As TDOAs require intersecting hyperboloids, which is
considered a hard task, this paper follows a different ap-
proach, using raw receiving times. Thus, estimating the
signal's emission time is required, captured as a com-
mon offset within an augmented version of the system
state. This way, the multilateration problem is reduced
to intersecting cones. Estimation of the aircraft's posi-
tion based on a nonlinear measurement model and an
underlying linear system model is achieved using a lin-
ear regression Kalman filter [1, 2]. A decomposed com-
putation of the filter step is introduced, allowing a more
efficient calculation.
Henning Eberhardt, Vesa Klumpp, Uwe D. Hanebeck,
Density Trees for Efficient Nonlinear State Estimation
Proceedings of the 13th International Conference on Information Fusion (Fusion 2010), Edinburgh, United Kingdom, July 2010.
PDF
Author : Henning Eberhardt, Vesa Klumpp, Uwe D. Hanebeck
Title : Density Trees for Efficient Nonlinear State Estimation
In : Proceedings of the 13th International Conference on Information Fusion (Fusion 2010)
Address : Edinburgh, United Kingdom
Date : July 2010
Abstract
In this paper, a new class of nonlinear Bayesian
estimators based on a special space partitioning structure, generalized
Octrees, is presented. This structure minimizes memory and calculation
overhead. It is used as a container framework for a set of node functions
that approximate a density piecewise. All necessary operations are derived
in a very general way in order to allow for a great variety of Bayesian
estimators. The presented estimators are especially well suited for
multi-modal nonlinear estimation problems. The running time performance
of the resulting estimators is first analyzed theoretically and then backed
by means of simulations. All operations have a linear running time in
the number of tree nodes.
Vesa Klumpp, Frederik Beutler, Uwe D. Hanebeck, Dietrich Fränken,
The Sliced Gaussian Mixture Filter with Adaptive State Decomposition Depending on Linearization Error
Proceedings of the 13th International Conference on Information Fusion (Fusion 2010), Edinburgh, United Kingdom, July 2010.
PDF
Author : Vesa Klumpp, Frederik Beutler, Uwe D. Hanebeck, Dietrich Fränken
Title : The Sliced Gaussian Mixture Filter with Adaptive State Decomposition Depending on Linearization Error
In : Proceedings of the 13th International Conference on Information Fusion (Fusion 2010)
Address : Edinburgh, United Kingdom
Date : July 2010
Abstract
In this paper, a novel nonlinear/non-linear model
decomposition for the Sliced Gaussian Mixture Filter is presented.
Based on the level of nonlinearity of the model, the overall estimation
problem is decomposed into a severely nonlinear and a slightly
nonlinear part, which are processed by different estimation techniques.
To further improve the efficiency of the estimator, an adaptive state
decomposition algorithm is introduced that allows decomposition
according to the linearization error for nonlinear system and
measurement models. Simulations show that this approach has orders of
magnitude less complexity compared to other state of the art
estimators, while maintaining comparable estimation errors.
Vesa Klumpp, Benjamin Noack, Marcus Baum, Uwe D. Hanebeck,
Combined Set-Theoretic and Stochastic Estimation: A Comparison of the SSI and the CS Filter
Proceedings of the 13th International Conference on Information Fusion (Fusion 2010), Edinburgh, United Kingdom, July 2010.
PDF
Author : Vesa Klumpp, Benjamin Noack, Marcus Baum, Uwe D. Hanebeck
Title : Combined Set-Theoretic and Stochastic Estimation: A Comparison of the SSI and the CS Filter
In : Proceedings of the 13th International Conference on Information Fusion (Fusion 2010)
Address : Edinburgh, United Kingdom
Date : July 2010
Abstract
In estimation theory, mainly set-theoretic or
stochastic uncertainty is considered. In some cases, especially when
some statistics of a distribution are not known or additional
stochastic information is used in a set-theoretic estimator, both
types of uncertainty have to be considered. In this paper, two
estimators that cope with combined stoachastic and set-theoretic
uncertainty are compared, namely the Set-theoretic and Statistical
Information filter, which represents the uncertainty by means of
random sets, and the Credal State filter, in which the state
information is given by sets of probability density functions.
The different uncertainty assessment in both estimators leads to
different estimation results, even when the prior information and
the measurement and system models are equal. This paper explains
these differences and states directions, when which estimator
should be applied to a given estimation problem.
Peter Krauthausen, Marco F. Huber, Uwe D. Hanebeck,
Support-Vector Conditional Density Estimation for Nonlinear Filtering
Proceedings of the 13th International Conference on Information Fusion (Fusion 2010), Edinburgh, United Kingdom, July 2010.
PDF
Author : Peter Krauthausen, Marco F. Huber, Uwe D. Hanebeck
Title : Support-Vector Conditional Density Estimation for Nonlinear Filtering
In : Proceedings of the 13th International Conference on Information Fusion (Fusion 2010)
Address : Edinburgh, United Kingdom
Date : July 2010
Abstract
A non-parametric conditional density
estimation algorithm for nonlinear stochastic dynamic
systems is proposed. The contributions are a novel sup-
port vector regression for estimating conditional den-
sities, modeled by Gaussian mixture densities, and an
algorithm based on cross-validation for automatically
determining hyper-parameters for the regression. The
conditional densities are employed with a modi?ed axis-
aligned Gaussian mixture filter. The experimental va-
lidation shows the high quality of the conditional densi-
ties and good accuracy of the proposed filter.
Benjamin Noack, Vesa Klumpp, Nikolay Petkov, Uwe D. Hanebeck,
Bounding Linearization Errors with Sets of Densities in Approximate Kalman Filtering
Proceedings of the 13th International Conference on Information Fusion (Fusion 2010), Edinburgh, United Kingdom, July 2010.
PDF
Author : Benjamin Noack, Vesa Klumpp, Nikolay Petkov, Uwe D. Hanebeck
Title : Bounding Linearization Errors with Sets of Densities in Approximate Kalman Filtering
In : Proceedings of the 13th International Conference on Information Fusion (Fusion 2010)
Address : Edinburgh, United Kingdom
Date : July 2010
Abstract
Applying the Kalman filtering scheme to linearized system dynamics and observation models does in general not yield optimal state estimates.
More precisely, inconsistent state estimates and covariance matrices are caused by neglected linearization errors.
This paper introduces a concept for systematically predicting and updating bounds for the linearization errors within the Kalman filtering framework.
To achieve this, an uncertain quantity is not characterized by a single probability density anymore, but rather by a set of densities and accordingly,
the linear estimation framework is generalized in order to process sets of probability densities. By means of this generalization,
the Kalman filter may then not only be applied to stochastic quantities, but also to unknown but bounded quantities.
In order to improve the reliability of Kalman filtering results, the last-mentioned quantities are utilized to bound the typically neglected nonlinear parts of a linearized mapping.
Daniel Lyons, Benjamin Noack, Uwe D. Hanebeck,
A Log-Ratio Information Measure for Stochastic Sensor Management
Proceedings of the IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing (SUTC 2010), Newport Beach, California, USA, June 2010.
PDF
Author : Daniel Lyons, Benjamin Noack, Uwe D. Hanebeck
Title : A Log-Ratio Information Measure for Stochastic Sensor Management
In : Proceedings of the IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing (SUTC 2010)
Address : Newport Beach, California, USA
Date : June 2010
Abstract
In distributed sensor networks, computational and energy resources are
in general limited. Therefore, an intelligent selection of sensors for
measurements is of great importance to ensure both high estimation
quality and an extended lifetime of the network. Methods from the theory
of model predictive control together with information theoretic measures
have been employed to pick sensors yielding measurements with high
information value. We present a novel information measure that originates from a
scalar product on a class of continuous probability densities and apply it
to the field of sensor management. Aside from its mathematical justifications
for quantifying the information content of probability densities, the most
remarkable property of the measure, an analogon of the triangle inequality
under Bayesian information fusion, is deduced. This allows for deriving
computationally cheap upper bounds for the model predictive sensor selection
algorithm and for comparing the performance of planning over different lengths of time horizons.
Henning Eberhardt, Vesa Klumpp, Uwe D. Hanebeck,
Optimal Dirac Approximation by Exploiting Independencies
Proceedings of the 2010 American Control Conference (ACC 2010), Baltimore, Maryland, USA, June 2010.
PDF
Author : Henning Eberhardt, Vesa Klumpp, Uwe D. Hanebeck
Title : Optimal Dirac Approximation by Exploiting Independencies
In : Proceedings of the 2010 American Control Conference (ACC 2010)
Address : Baltimore, Maryland, USA
Date : June 2010
Abstract
The sample-based recursive prediction of discrete-time nonlinear
stochastic dynamic systems requires a regular reapproximation of the Dirac mixture
densities characterizing the state estimate with an exponentially increasing number
of components. For that purpose, a systematic approximation method is proposed that
is deterministic and guaranteed to minimize a new type distance measure, the so
called modified Cram\\\\ér-von Mises distance. A huge increase in approximation
performance is achieved by exploiting structural independencies usually occurring
between the random variables used as input to the system. The corresponding prediction
step achieves optimal performance when no further assumptions can be made about the
system function. In addition, the proposed approach shows a much better convergence
compared to the prediction step of the particle filter and by far fewer Dirac components
are required for achieving a given approximation quality. As a result, the new
approximation method opens the way for the development of new fully deterministic and
optimal stochastic state estimators for nonlinear dynamic systems.
Peter Krauthausen, Uwe D. Hanebeck,
Parameter Learning for Hybrid Bayesian Networks With Gaussian Mixture and Dirac Mixture Conditional Densities
Proceedings of the 2010 American Control Conference (ACC 2010), Baltimore, Maryland, USA, June 2010.
PDF
Author : Peter Krauthausen, Uwe D. Hanebeck
Title : Parameter Learning for Hybrid Bayesian Networks With Gaussian Mixture and Dirac Mixture Conditional Densities
In : Proceedings of the 2010 American Control Conference (ACC 2010)
Address : Baltimore, Maryland, USA
Date : June 2010
Abstract
In this paper, the first algorithm for learning hybrid Bayesian
Networks with Gaussian mixture and Dirac mixture conditional densities from data
given their structure is presented. The mixture densities to be learned allow for
nonlinear dependencies between the variables and exact closedform inference. For
learning the network's parameters, an incremental gradient ascent algorithm is derived.
Analytic expressions for the partial derivatives and their combination with messages are
presented. This hybrid approach subsumes the existing approach for purely discrete-valued
networks and is applicable to partially observable networks, too. Its practicability is
demonstrated by a reference example.
Daniel Lyons, Achim Hekler, Benjamin Noack, Uwe D. Hanebeck,
Maße für Wahrscheinlichkeitsdichten in der informationstheoretischen Sensoreinsatzplanung
Verteilte Messsysteme, pp. 121-132, KIT Scientific Publishing, March 2010.
URL
Author : Daniel Lyons, Achim Hekler, Benjamin Noack, Uwe D. Hanebeck
Title : Maße für Wahrscheinlichkeitsdichten in der informationstheoretischen Sensoreinsatzplanung
In : Verteilte Messsysteme
Address :
Date : March 2010
Abstract
Bei der Beobachtung eines räumlich verteilten Phänomens mit einer
Vielzahl von Sensoren ist die intelligente Auswahl von Messkonfigurationen aufgrund von
beschränkten Rechen- und Kommunikationskapazitäten entscheidend für die
Lebensdauer des Sensornetzes. Mit der Sensoreinsatzplanung kann die im nächsten
Zeitschritt anzusteuernde Messkonfiguration dynamisch mittels einer stochastischen
modell-prädiktiven Planung über einen endlichen Zeithorizont bestimmt werden.
Dabei wird als Gütekriterium die Maximierung des zu erwartenden Informationsgewinns
durch zukünftige Messungen unter sparsamer Verwendung der Energieressourcen gewählt.
In diesem Artikel wird ein neues Maß für kontinuierliche Wahrscheinlichkeitsdichten
vorgestellt, das sich kanonisch aus der Konstruktion eines Vektorraums für
Wahrscheinlichkeitsdichten ergibt. Dieses Maß wird als Gütefunktion in der
vorausschauenden Sensoreinsatzplanung zur Bewertung des informationstheoretischen Einfluß
von Messungen auf die aktuelle Zustandsschätzung verwendet.
Benjamin Noack, Vesa Klumpp, Daniel Lyons, Uwe D. Hanebeck,
Systematische Beschreibung von Unsicherheiten in der Informationsfusion mit Mengen von Wahrscheinlichkeitsdichten
Verteilte Messsysteme, pp. 167-178, KIT Scientific Publishing, March 2010.
URL
Author : Benjamin Noack, Vesa Klumpp, Daniel Lyons, Uwe D. Hanebeck
Title : Systematische Beschreibung von Unsicherheiten in der Informationsfusion mit Mengen von Wahrscheinlichkeitsdichten
In : Verteilte Messsysteme
Address :
Date : March 2010
Abstract
Die systematische Behandlung von Unsicherheiten stellt eine wesentliche
Herausforderung in der Informationsfusion dar. Einerseits müssen geeignete Darstellungsformen
für die Unsicherheiten bestimmt werden und andererseits darauf aufbauend effiziente
Schätzverfahren hergeleitet werden. Im Allgemeinen wird zwischen stochastischen und
mengenbasierten Unsicherheitsbeschreibungen unterschieden. Dieser Beitrag stellt ein Verfahren
zur Zustandsschätzung vor, welches simultan stochastische und mengenbasierte Fehlergrößen
berücksichtigen kann, indem unsichere Größen nicht mehr durch eine einzelne
Wahrscheinlichkeitsdichte, sondern durch eine Menge von Dichten repräsentiert werden.
Besonderes Augenmerk liegt hier auf den Vorteilen und Anwendungsmöglichkeiten dieser
Unsicherheitsbeschreibung.
Achim Kuwertz, Marco F. Huber, Felix Sawo, Uwe D. Hanebeck,
Sensoreinsatzplanung zur Verfolgung von Quellen räumlich ausgedehnter Phänomene
Verteilte Messsysteme, pp. 179-191, KIT Scientific Publishing, March 2010.
URL
Author : Achim Kuwertz, Marco F. Huber, Felix Sawo, Uwe D. Hanebeck
Title : Sensoreinsatzplanung zur Verfolgung von Quellen räumlich ausgedehnter Phänomene
In : Verteilte Messsysteme
Address :
Date : March 2010
Abstract
Räumlich ausgedehnte Phänomene wie Schadstoffverteilungen in Gewässern
oder Temperaturverteilungen in Räumen werden vielfach durch unbekannte, aber gegebenenfalls
sich bewegende Quellen erzeugt. Allerdings stehen in vielen praktisch relevanten Aufgabenstellungen
Informationen zur Lokalisierung einer derartigen Quelle nur indirekt durch eine Vermessung des
induzierten Phänomens zur Verfügung, welche den Einsatz eines verteilten Messsystems erfordert.
Die Messungen können dabei beispielsweise von einem stationären Sensornetz oder von mobilen
Sensoren stammen. In diesem Beitrag werden modellbasierte Verfahren zu echtzeitfähigen Lokalisierung
und schritthaltenden Verfolgung von Quellen vorgestellt, welche gezielt räumlich und zeitlich
verteilte Messungen einsetzen. Um den Informationsgewinn und somit den Nutzen verteilter Messungen zu
maximieren, spielt bei diesem Verfahren neben einer mathematischen Modellierung auch eine vorausschauende
Sensoreinsatzplanung eine zentrale Rolle. Das in diesem Beitrag vorgeschlagene Planungsverfahren
ermöglicht dabei die effiziente und ressourcenschonende Verfolgung beweglicher Quellen bei gleichzeitig
hoher Lokalisierungsgenauigkeit.
Antonia Pérez Arias, Tobias Kretz, Peter Ehrhardt, Stefan Hengst, Peter Vortisch, Uwe D. Hanebeck,
Extended Range Telepresence for Evacuation Training in Pedestrian Simulations
Proccedings of the 5th International Conference on Pedestrian and Evacuation Dynamics (PED 2010), Springer-Verlag, Gaithersburg, Maryland, USA, March 2010.
PDF
Author : Antonia Pérez Arias, Tobias Kretz, Peter Ehrhardt, Stefan Hengst, Peter Vortisch, Uwe D. Hanebeck
Title : Extended Range Telepresence for Evacuation Training in Pedestrian Simulations
In : Proccedings of the 5th International Conference on Pedestrian and Evacuation Dynamics (PED 2010), Springer-Verlag
Address : Gaithersburg, Maryland, USA
Date : March 2010
Abstract
In this contribution, we propose a new framework to evaluate pedestrian
simulations by using Extended Range Telepresence. Telepresence is
used as a virtual reality walking simulator, which provides the user
with a realistic impression of being present and walking in a virtual
environment that is much larger than the real physical environment,
in which the user actually walks. The validation of the simulation
is performed by comparing motion data of the telepresent user with
simulated data at some points of the simulation. The use of haptic
feedback from the simulation makes the framework suitable for training
in emergency situations.

Publikationen aus dem Jahr 2009

Marcus Baum, Uwe D. Hanebeck,
Random Hypersurface Models for Extended Object Tracking
Proceedings of the 9th IEEE International Symposium on Signal Processing and Information Technology (ISSPIT 2009), Ajman, United Arab Emirates, December 2009.
PDF
Author : Marcus Baum, Uwe D. Hanebeck
Title : Random Hypersurface Models for Extended Object Tracking
In : Proceedings of the 9th IEEE International Symposium on Signal Processing and Information Technology (ISSPIT 2009)
Address : Ajman, United Arab Emirates
Date : December 2009
Abstract
Target tracking algorithms usually assume that the received measurements
stem from a point source. However, in many scenarios this assumption
is not feasible so that measurements may stem from different locations,
named measurement sources, on the target surface. Then, it is necessary
to incorporate the target extent into the estimation procedure in
order to obtain robust and precise estimation results. This paper
introduces the novel concept of Random Hypersurface Models for extended
targets. A Random Hypersurface Model assumes that each measurement
source is an element of a randomly generated hypersurface. The applicability
of this approach is demonstrated by means of an elliptic target shape.
In this case, a Random Hypersurface Model specifies the random (relative)
Mahalanobis distance of a measurement source to the center of the
target object. As a consequence, good estimation results can be obtained
even if the true target shape significantly differs from the modeled
shape. Additionally, Random Hypersurface Models are computationally
tractable with standard nonlinear stochastic state estimators.
Uwe D. Hanebeck, Marco F. Huber, Vesa Klumpp,
Dirac Mixture Approximation of Multivariate Gaussian Densities
Proceedings of the 2009 IEEE Conference on Decision and Control (CDC 2009), Shanghai, China, December 2009.
PDF
Author : Uwe D. Hanebeck, Marco F. Huber, Vesa Klumpp
Title : Dirac Mixture Approximation of Multivariate Gaussian Densities
In : Proceedings of the 2009 IEEE Conference on Decision and Control (CDC 2009)
Address : Shanghai, China
Date : December 2009
Abstract
For the optimal approximation of multivariate
Gaussian densities by means of Dirac mixtures, i.e., by means of
a sum of weighted Dirac distributions on a continuous domain,
a novel systematic method is introduced. The parameters of
this approximate density are calculated by minimizing a global
distance measure, a generalization of the well-known Cram\\\\ér-
von Mises distance to the multivariate case. This generalization
is obtained by defining an alternative to the classical cumulative
distribution, the Localized Cumulative Distribution (LCD). In
contrast to the cumulative distribution, the LCD is unique
and symmetric even in the multivariate case. The resulting
deterministic approximation of Gaussian densities by means of
discrete samples provides the basis for new types of Gaussian
filters for estimating the state of nonlinear dynamic systems
from noisy measurements.
Evgeniya Bogatyrenko, Uwe D. Hanebeck, Gabor Szabo,
Heart Surface Motion Estimation Framework for Robotic Surgery Employing Meshless Methods
Proceedings of the 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2009), October 2009.
PDF
Author : Evgeniya Bogatyrenko, Uwe D. Hanebeck, Gabor Szabo
Title : Heart Surface Motion Estimation Framework for Robotic Surgery Employing Meshless Methods
In : Proceedings of the 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2009)
Address :
Date : October 2009
Abstract
A novel heart surface motion estimation frame-
work for a robotic surgery on a stabilized beating heart is
proposed. It includes an approach for the reconstruction and
prediction of heart surface motion based on a novel physical
model of the intervention area described by a distributed-
parameter system. Instead of conventional element methods, a
meshless method is used for a spatial and temporal decomposi-
tion of this system. This leads to a finite-dimensional state-space
form. Furthermore, the state of the resulting lumped-parameter
system, which provides an approximation of the deflection and
velocity of the heart surface, is dynamically estimated under
consideration of uncertainties both occurring in the system
and arising from noisy camera measurements. By using the
estimation results, an accurate reconstruction of heart surface
motion for the synchronisation of the surgical instruments is
also achieved at occluded or non-measurement points.
Marcus Baum, Uwe D. Hanebeck,
Tracking an Extended Object Modeled as an Axis-Aligned Rectangle
4th German Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF 2009), 39th Annual Conference of the Gesellschaft für Informatik e.V. (GI), Lübeck, Germany, October 2009.
PDF
Author : Marcus Baum, Uwe D. Hanebeck
Title : Tracking an Extended Object Modeled as an Axis-Aligned Rectangle
In : 4th German Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF 2009), 39th Annual Conference of the Gesellschaft für Informatik e.V. (GI)
Address : Lübeck, Germany
Date : October 2009
Abstract
In many tracking applications, the extent of the target object is neglected
and it is assumed that the received measurements stem from a point source. However,
modern sensors are able to supply several measurements from different scattering cen-
ters on the target object due to their high-resolution capability. As a consequence, it
becomes necessary to incorporate the target extent into the estimation procedure. This
paper introduces a new method for tracking the smallest enclosing rectangle of an ex-
tended object with an unknown shape. At each time step, a finite set of noisy position
measurements that stem from arbitrary, unknown measurement sources on the target
surface may be available. In contrast to common approaches, the presented approach
does not have to make any statistical assumptions on the measurement sources.
Andreas Rauh, Kai Briechle, Uwe D. Hanebeck,
Nonlinear Measurement Update and Prediction: Prior Density Splitting Mixture Estimator
Proceedings of the 2009 IEEE International Conference on Control Applications (CCA 2009), July 2009.
PDF
Author : Andreas Rauh, Kai Briechle, Uwe D. Hanebeck
Title : Nonlinear Measurement Update and Prediction: Prior Density Splitting Mixture Estimator
In : Proceedings of the 2009 IEEE International Conference on Control Applications (CCA 2009)
Address :
Date : July 2009
Abstract
In this paper, the Prior Density Splitting Mixture
Estimator (PDSME), a new Gaussian mixture filtering
algorithm for nonlinear dynamical systems and nonlinear
measurement equations, is introduced. This filter reduces the
linearization error which typically arises if nonlinear state and
measurement equations are linearized to apply linear filtering
techniques. For that purpose, the PDSME splits the prior probability
density into several components of a Gaussian mixture
with smaller covariances. The PDSME is applicable to both
prediction and filter steps. A measure for the linearization error
similar to the Kullback-Leibler distance is introduced allowing
the user to specify the desired estimation quality. An upper
bound for the computational effort can be given by limiting
the maximum number of Gaussian mixture components.
Marcus Baum, Uwe D. Hanebeck,
Extended Object Tracking based on Combined Set-Theoretic and Stochastic Fusion
Proceedings of the 12th International Conference on Information Fusion (Fusion 2009), Seattle, Washington, USA, July 2009.
PDF
Author : Marcus Baum, Uwe D. Hanebeck
Title : Extended Object Tracking based on Combined Set-Theoretic and Stochastic Fusion
In : Proceedings of the 12th International Conference on Information Fusion (Fusion 2009)
Address : Seattle, Washington, USA
Date : July 2009
Abstract
In this paper, a novel approach for tracking
extended objects is presented. The target object is
modeled as a circular disc such that the center and
extent of the target object can be estimated. At each
time step, a finite set of position measurements that
are corrupted with stochastic noise may be available.
Each position measurement stems from an unknown measurement
source on the extended object. In contrast to existing
approaches, no statistical assumptions about the distribution
of the measurement sources on the extended object are made.
As a consequence, it is necessary to deal with stochastic
and set-valued uncertainties. For this purpose, a novel
combined stochastic and set-theoretic estimator that employs
random hyperboloids to express the uncertainties about the
true circular disc is derived.
Frederik Beutler, Marco F. Huber, Uwe D. Hanebeck,
Gaussian Filtering using State Decomposition Methods
Proceedings of the 12th International Conference on Information Fusion (Fusion 2009), Seattle, Washington, USA, July 2009.
PDF
Author : Frederik Beutler, Marco F. Huber, Uwe D. Hanebeck
Title : Gaussian Filtering using State Decomposition Methods
In : Proceedings of the 12th International Conference on Information Fusion (Fusion 2009)
Address : Seattle, Washington, USA
Date : July 2009
Abstract
State estimation for nonlinear systems generally
requires approximations of the system or the probability
densities, as the occurring prediction and filtering equations
cannot be solved in closed form. For instance, Linear Regression
Kalman Filters like the Unscented Kalman Filter
or the considered Gaussian Filter propagate a small set of
sample points through the system to approximate the posterior
mean and covariance matrix. To reduce the number of
sample points, special structures of the system and measurement
equation can be taken into account. In this paper, two
principles of system decomposition are considered and applied
to the Gaussian Filter. One principle exploits that only
a part of the state vector is directly observed by the measurement.
The second principle separates the system equations
into linear and nonlinear parts in order to merely approximate
the nonlinear part of the state. The benefits of both
decompositions are demonstrated on a real-world example.
Julian Hörst, Felix Sawo, Vesa Klumpp, Uwe D. Hanebeck, Dietrich Fränken,
Extension of the Sliced Gaussian Mixture Filter with Application to Cooperative Passive Target Tracking
Proceedings of the 12th International Conference on Information Fusion (Fusion 2009), Seattle, Washington, USA, July 2009.
PDF
Author : Julian Hörst, Felix Sawo, Vesa Klumpp, Uwe D. Hanebeck, Dietrich Fränken
Title : Extension of the Sliced Gaussian Mixture Filter with Application to Cooperative Passive Target Tracking
In : Proceedings of the 12th International Conference on Information Fusion (Fusion 2009)
Address : Seattle, Washington, USA
Date : July 2009
Abstract
This paper copes with the problem of nonlinear Bayesian state estimation.
A nonlinear filter, the Sliced Gaussian Mixture Filter (SGMF), employs linear
substructures in the nonlinear measurement and prediction model in order to
simplify the estimation process.
Here, a special density representation, the sliced Gaussian mixture
density, is used to derive an exact solution of the Chapman-Kolmogorov equation.
The sliced Gaussian mixture density is obtained by a systematic and deterministic
approximation of a continuous density minimizing a certain distance measure.
In contrast to previous work, improvements of the SGMF presented here include an
extended system model and the processing of multi-dimensional nonlinear
subspaces. As an application for the SGMF, cooperative passive target tracking,
where sensors take angular measurements from a target, is considered in this paper.
Finally, the performance of the proposed estimator is compared to the
marginalized particle filter (MPF) in simulations.
Marco F. Huber, Achim Kuwertz, Felix Sawo, Uwe D. Hanebeck,
Distributed Greedy Sensor Scheduling for Model-based Reconstruction of Space-Time Continuous Physical Phenomena
Proceedings of the 12th International Conference on Information Fusion (Fusion 2009), Seattle, Washington, USA, July 2009.
PDF
Author : Marco F. Huber, Achim Kuwertz, Felix Sawo, Uwe D. Hanebeck
Title : Distributed Greedy Sensor Scheduling for Model-based Reconstruction of Space-Time Continuous Physical Phenomena
In : Proceedings of the 12th International Conference on Information Fusion (Fusion 2009)
Address : Seattle, Washington, USA
Date : July 2009
Abstract
A novel distributed sensor scheduling method for large-scale sensor
networks observing space-time continuous physical phenomena is
introduced. In a first step, the model of the distributed phenomenon
is spatially and temporally decomposed leading to a linear
probabilistic finite-dimensional model. Based on this representation,
the information gain of sensor measurements is evaluated by means of
the so-called covariance reduction function. For this reward function,
it is shown that the performance of the greedy sensor scheduling is at
least half that of the optimal scheduling considering long-term
effects. This finding is the key for distributed sensor scheduling,
where a central processing unit or fusion center is unnecessary, and
thus, scaling as well as reliability is ensured. Hence, greedy
scheduling in combination with a proposed hierarchical communication
scheme requires only local sensor information and communication.
Vesa Klumpp, Uwe D. Hanebeck,
Nonlinear Fusion of Multi-Dimensional Densities in Joint State Space
Proceedings of the 12th International Conference on Information Fusion (Fusion 2009), Seattle, Washington, USA, July 2009.
PDF
Author : Vesa Klumpp, Uwe D. Hanebeck
Title : Nonlinear Fusion of Multi-Dimensional Densities in Joint State Space
In : Proceedings of the 12th International Conference on Information Fusion (Fusion 2009)
Address : Seattle, Washington, USA
Date : July 2009
Abstract
Nonlinear fusion of multi-dimensional densities
is an important application in Bayesian state estimation.
In the approach proposed here, a joint density over all
considered densities is build, which is then approximated
by means of a Dirac mixture density by partitioning the
joint state space into regions that are represented by single
Dirac components. This approximation procedure depends
on the nonlinear fusion model and only areas relevant to this
model are considered. The processing in joint state space
has advantages, especially when fusing Dirac mixture densities.
Within this approach, degeneration can be avoided
and even densities without mutual support can be combined.
Thus, this approach gives an alternative to multiplication of
Dirac mixtures with a likelihood, as used in the particle filter.
Furthermore, a nonlinear Bayesian estimator with filter
and prediction step can be formulated, which is able to cope
with both discrete and continuous densities.
Vesa Klumpp, Uwe D. Hanebeck,
Bayesian Estimation with Uncertain Parameters of Probability Density Functions
Proceedings of the 12th International Conference on Information Fusion (Fusion 2009), Seattle, Washington, USA, July 2009.
PDF
Author : Vesa Klumpp, Uwe D. Hanebeck
Title : Bayesian Estimation with Uncertain Parameters of Probability Density Functions
In : Proceedings of the 12th International Conference on Information Fusion (Fusion 2009)
Address : Seattle, Washington, USA
Date : July 2009
Abstract
In this paper, we address the problem of
processing imprecisely known probability density func-
tions by means of Bayesian estimation. The imprecise
knowledge about probability density functions is given
as stochastic uncertainty about their parameters. The
proposed processing of this special density in a Bayesian
estimator is accomplished by reinterpretation of the Fil-
ter and prediction equations. Here, the parameters are
treated as a higher order state, which can be processed
by Bayesian estimation techniques. For state estima-
tion, this avoids the need to select specific values for
unknown parameters and, thus, allows the processing of
all potential parameters at once. The proposed approach
further allows the use of imprecisely known model equa-
tions for measurement and state prediction by the same
principle.
Peter Krauthausen, Uwe D. Hanebeck,
Intention Recognition for Partial-Order Plans Using Dynamic Bayesian Networks
Proceedings of the 12th International Conference on Information Fusion (Fusion 2009), Seattle, Washington, USA, July 2009.
PDF
Author : Peter Krauthausen, Uwe D. Hanebeck
Title : Intention Recognition for Partial-Order Plans Using Dynamic Bayesian Networks
In : Proceedings of the 12th International Conference on Information Fusion (Fusion 2009)
Address : Seattle, Washington, USA
Date : July 2009
Abstract
In this paper, a novel probabilistic ap-
proach to intention recognition for partial-order plans
is proposed. The key idea is to exploit independences
between subplans to substantially reduce the state space
sizes in the compiled Dynamic Bayesian Networks.
This makes inference more efficient. The main con-
tributions are the computationally exploitable definition
of subplan structures, the introduction of a novel Lay-
ered Intention Model and a Dynamic Bayesian Net-
work representation with an inference mechanism that
exploits consecutive and concurrent subplans' indepen-
dences. The presented approach reduces the state space
to the order of the most complex subplan and requires
only minor changes in the standard inference mecha-
nism. The practicability of this approach is demon-
strated by recognizing the process of shelf-assembly.
Benjamin Noack, Vesa Klumpp, Uwe D. Hanebeck,
State Estimation with Sets of Densities considering Stochastic and Systematic Errors
Proceedings of the 12th International Conference on Information Fusion (Fusion 2009), Seattle, Washington, USA, July 2009.
PDF
Author : Benjamin Noack, Vesa Klumpp, Uwe D. Hanebeck
Title : State Estimation with Sets of Densities considering Stochastic and Systematic Errors
In : Proceedings of the 12th International Conference on Information Fusion (Fusion 2009)
Address : Seattle, Washington, USA
Date : July 2009
Abstract
In practical applications, state estimation requires the consideration of
stochastic and systematic errors. If both error types are present, an exact
probabilistic description of the state estimate is not possible, so that
common Bayesian estimators have to be questioned. This paper introduces a
theoretical concept, which allows for incorporating unknown but bounded errors
into a Bayesian inference scheme by utilizing sets of densities. In order to
derive a tractable estimator, the Kalman filter is applied to ellipsoidal sets
of means, which are used to bound additive systematic errors. Also, an
extension to nonlinear system and observation models with ellipsoidal error
bounds is presented. The derived estimator is motivated by means of two
example applications.
Antonia Pérez Arias, Uwe D. Hanebeck,
A Novel Haptic Interface for Extended Range Telepresence: Control and Evaluation
Proceedings of the 6th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2009), pp. 222-227, Milan, Italy, July 2009.
PDF
Author : Antonia Pérez Arias, Uwe D. Hanebeck
Title : A Novel Haptic Interface for Extended Range Telepresence: Control and Evaluation
In : Proceedings of the 6th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2009)
Address : Milan, Italy
Date : July 2009
Abstract
A novel haptic interface for extended range telepresence
is presented that allows the user simultaneous wide area motion and
haptic interaction in remote environments. To achieve an extended
workspace, the haptic interface consists of a haptic manipulator for
precise haptic rendering and a large portal carrier system that
enlarges the workspace by prepositioning the end-effector. As the
prepositioning unit is grounded and driven by three linear drives, our
approach has the advantages of high force capability, and an accurate
positioning of the haptic interface. The use of this haptic interface
with Motion Compression permits to explore large remote environments
even from small user environments. As a result, not only has the user
visual, acoustic, and haptic feedback but can also control the
teleoperator or avatar by natural walking, which considerably increases
the sense of immersion. A prototype system for haptic extended range
telepresence was designed, implemented, and tested.
Marc P. Deisenroth, Marco F. Huber, Uwe D. Hanebeck,
Analytic Moment-based Gaussian Process Filtering
26th International Conference on Machine Learning (ICML 2009) in Montreal, Canada, June 2009.
PDF
Author : Marc P. Deisenroth, Marco F. Huber, Uwe D. Hanebeck
Title : Analytic Moment-based Gaussian Process Filtering
In : 26th International Conference on Machine Learning (ICML 2009) in Montreal, Canada
Address :
Date : June 2009
Abstract
We propose an analytic moment-based filter for nonlinear stochastic
dynamic systems modeled by Gaussian processes. Exact expressions for the
expected value and the covariance matrix are provided for both the
prediction step and the filter step, where an additional Gaussian
assumption is exploited in the latter case. Our filter does not require
further approximations. In particular, it avoids finite-sample
approximations. We compare the filter to a variety of Gaussian filters,
that is, the EKF, the UKF, and the recent GP-UKF proposed by Ko et al.
(2007).
Antonia Pérez Arias, Tobias Kretz, Peter Ehrhardt, Stefan Hengst, Peter Vortisch, Uwe D. Hanebeck,
A Framework for Evaluating the VISSIM Traffic Simulation with Extended Range Telepresence
Proceedings of the 22nd Annual Conference on Computer Animation and Social Agents (CASA 2009), pp. 13-16, Amsterdam, The Netherlands, June 2009.
PDF
Author : Antonia Pérez Arias, Tobias Kretz, Peter Ehrhardt, Stefan Hengst, Peter Vortisch, Uwe D. Hanebeck
Title : A Framework for Evaluating the VISSIM Traffic Simulation with Extended Range Telepresence
In : Proceedings of the 22nd Annual Conference on Computer Animation and Social Agents (CASA 2009)
Address : Amsterdam, The Netherlands
Date : June 2009
Abstract
This paper presents a novel framework
for combining traffic simulations and extended range
telepresence. The real user's position data can thus
be used for validation and calibration of models of
pedestrian dynamics, while the user experiences a
high degree of immersion by interacting with agents
in realistic simulations.
Frederik Beutler, Marco F. Huber, Uwe D. Hanebeck,
Instantaneous Pose Estimation using Rotation Vectors
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2009) in Taipei, Taiwan, pp. 3413-3416, April 2009.
PDF
Author : Frederik Beutler, Marco F. Huber, Uwe D. Hanebeck
Title : Instantaneous Pose Estimation using Rotation Vectors
In : IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2009) in Taipei, Taiwan
Address :
Date : April 2009
Abstract
An algorithm for estimating the pose, i.e., translation and rotation, of
an extended target object is introduced. Compared to conventional
methods, where pose estimation is performed on the basis of timeof-
flight (TOF) measurements between external sources and sensors
attached to the object, the proposed approach directly uses the amplitude
values measured at the sensors for estimation purposes without
an intermediate TOF estimation step. This is achieved by modeling
the wave propagation by a nonlinear dynamic system comprising a
system and a measurement equation. The nonlinear system equation
includes a model of the time-variant structure of the object rotation
based on rotation vectors. As a result, the measured amplitude values
at the sensors can be processed instantaneously in a recursive
fashion. Uncertainties in the measurement process are systematically
considered by employing a stochastic filter for estimating the
pose, i.e., the state of the nonlinear dynamic system.
Frederik Beutler, Marco F. Huber, Uwe D. Hanebeck,
Probabilistic Instantaneous Model-Based Signal Processing applied to Localization and Tracking
Journal of Robotics and Autonomous Systems, Selected papers from 2006 IEEE International Conference on Multisensor Fusion and Integration (MFI 2006), 57(3):249-258, March 2009.
URL
Author : Frederik Beutler, Marco F. Huber, Uwe D. Hanebeck
Title : Probabilistic Instantaneous Model-Based Signal Processing applied to Localization and Tracking
In : Journal of Robotics and Autonomous Systems, Selected papers from 2006 IEEE International Conference on Multisensor Fusion and Integration (MFI 2006)
Address :
Date : March 2009
Abstract
In this paper, a probabilistic approach for estimating time
and space-variant parameters of a system, based on sequentially received
discrete-time signal values, is presented. The system description is the
solution of a linear partial differential equation (PDE). The PDE describes
for example the wave propagation of an acoustic wave in a localization
system. The solution of the PDE is given by a time-variant and space-variant
impulse response. This impulse response is characterized by the time and
space-variant parameters in order to track an object, which emits for example
an acoustic signal. For estimating the position of the object in an
instantaneous way a Bayesian approach has to be used, which considers the
dynamic behavior of the parameters in a system model and uncertainties in a
stochastic manner by means of probability density functions. Hence, the new
approach provides a probabilistic instantaneous model-based signal processing,
where the sequentially measured signal values are processed directly and known
reference signal sequences are interpreted as part of a time-variant nonlinear
measurement equation.

Publikationen aus dem Jahr 2008

Oliver C. Schrempf, Uwe D. Hanebeck,
Dirac Mixture Approximation for Nonlinear Stochastic Filtering
Informatics in Control, Automation and Robotics - Selected Papers from the International Conference on Informatics in Control, Automation and Robotics 2007, 24:287-300, Springer, September 2008.
URL
Author : Oliver C. Schrempf, Uwe D. Hanebeck
Title : Dirac Mixture Approximation for Nonlinear Stochastic Filtering
In : Informatics in Control, Automation and Robotics - Selected Papers from the International Conference on Informatics in Control, Automation and Robotics 2007
Address :
Date : September 2008
Abstract
This work presents a filter for estimating the state of
nonlinear dynamic systems. It is based on optimal recursive approximation
the state densities by means of Dirac mixture functions in order to allow
for a closed form solution of the prediction and filter step. The
approximation approach is based on a systematic minimization of a distance
measure and is hence optimal and deterministic. In contrast to non-deterministic
methods we are able to determine the optimal number of components in the Dirac
mixture. A further benefit of the proposed approach is the consideration of
measurements during the approximation process in order to avoid parameter degradation.
Florian Weissel, Marco F. Huber, Uwe D. Hanebeck,
Stochastic Nonlinear Model Predictive Control based on Gaussian Mixture Approximations
Informatics in Control, Automation and Robotics - Selected Papers from the International Conference on Informatics in Control, Automation and Robotics 2007, 24:239-252, Springer, September 2008.
URL
Author : Florian Weissel, Marco F. Huber, Uwe D. Hanebeck
Title : Stochastic Nonlinear Model Predictive Control based on Gaussian Mixture Approximations
In : Informatics in Control, Automation and Robotics - Selected Papers from the International Conference on Informatics in Control, Automation and Robotics 2007
Address :
Date : September 2008
Abstract
In this paper, a framework for stochastic Nonlinear
Model Predictive Control (NMPC) that explicitly incorporates the
noise influence on systems with continuous state spaces is introduced.
By the incorporation of noise, which results from uncertainties during
model identification and measurement, the quality of control can be
significantly increased. Since stochastic NMPC requires the prediction
of system states over a certain horizon, an efficient state prediction
technique for nonlinear noise-affected systems is required. This is
achieved by using transition densities approximated by axis-aligned
Gaussian mixtures together with methods to reduce the computational burden.
A versatile cost function representation also employing Gaussianmixtures
provides an increased freedom of modeling. Combining the rediction technique
with this value function representation allows closed-form calculation of
the necessary optimization problems arising from stochastic NMPC. The
capabilities of the framework and especially the benefits that can be
gained by considering the noise in the controller are illustrated by the
example of a mobile robot following a given path.
Uwe D. Hanebeck, Vesa Klumpp,
Localized Cumulative Distributions and a Multivariate Generalization of the Cramér-von Mises Distance
Proceedings of the 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2008), pp. 33-39, Seoul, Republic of Korea, August 2008.
PDF
Author : Uwe D. Hanebeck, Vesa Klumpp
Title : Localized Cumulative Distributions and a Multivariate Generalization of the Cramér-von Mises Distance
In : Proceedings of the 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2008)
Address : Seoul, Republic of Korea
Date : August 2008
Abstract
This paper is concerned with distances for comparing
multivariate random vectors with a special focus on the case
that at least one of the random vectors is of discrete type, i.e.,
assumes values from a discrete set only. The first contribution
is a new type of characterization of multivariate random
quantities, the so called Localized Cumulative Distribution
(LCD) that, in contrast to the conventional definition of a
cumulative distribution, is unique and symmetric. Based on the
LCDs of the random vectors under consideration, the second
contribution is the definition of generalized distance measures
that are suitable for the multivariate case. These distances
are used for both analysis and synthesis purposes. Analysis
is concerned with assessing whether a given sample stems from
a given continuous distribution. Synthesis is concerned with
both density estimation, i.e., calculating a suitable continuous
approximation of a given sample, and density discretization,
i.e., approximation of a given continuous random vector by a
discrete one.
Marco F. Huber, Tim Bailey, Hugh Durrant-Whyte, Uwe D. Hanebeck,
On Entropy Approximation for Gaussian Mixture Random Vectors
Proceedings of the 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2008), pp. 181-188, Seoul, Republic of Korea, August 2008.
PDF
Author : Marco F. Huber, Tim Bailey, Hugh Durrant-Whyte, Uwe D. Hanebeck
Title : On Entropy Approximation for Gaussian Mixture Random Vectors
In : Proceedings of the 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2008)
Address : Seoul, Republic of Korea
Date : August 2008
Abstract
For many practical probability density representations
such as for the widely used Gaussian mixture densities, an
analytic evaluation of the differential entropy is not possible and
thus, approximate calculations are inevitable. For this purpose,
the first contribution of this paper deals with a novel entropy
approximation method for Gaussian mixture random vectors,
which is based on a component-wise Taylor-series expansion of
the logarithm of a Gaussian mixture and on a splitting method
of Gaussian mixture components. The employed order of the
Taylor-series expansion and the number of components used for
splitting allows balancing between accuracy and computational
demand. The second contribution is the determination of meaningful
and efficiently to calculate lower and upper bounds of the
entropy, which can be also used for approximation purposes.
In addition, a refinement method for the more important upper
bound is proposed in order to approach the true entropy value.
Vesa Klumpp, Uwe D. Hanebeck,
Direct Fusion of Dirac Mixture Densities using an Efficient Approximation in Joint State Space
Proceedings of the 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2008), pp. 168-174, Seoul, Republic of Korea, August 2008.
PDF
Author : Vesa Klumpp, Uwe D. Hanebeck
Title : Direct Fusion of Dirac Mixture Densities using an Efficient Approximation in Joint State Space
In : Proceedings of the 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2008)
Address : Seoul, Republic of Korea
Date : August 2008
Abstract
In this paper, we present a direct fusion algorithm
for processing the combination of two Dirac mixture densities.
The proposed approach allows the multiplication of two Dirac
mixture densities without requiring identical support and thus
enables the fusion of two independently generated sample sets.
The resulting posterior Dirac mixture density is an approximation
of the true continuous density that would result from the
processing of the underlying true continuous density functions.
This procedure is based on a suboptimal greedy approximation
of the joint state space by means of a Dirac mixture that
iteratively increases the resolution of the fusion result while
considering only the relevant regions in the joint state space,
where the fusion constraint holds.
Vesa Klumpp, Uwe D. Hanebeck,
Dirac Mixture Trees for Fast Suboptimal Multi-Dimensional Density Approximation
Proceedings of the 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2008), pp. 593-600, Seoul, Republic of Korea, August 2008.
PDF
Author : Vesa Klumpp, Uwe D. Hanebeck
Title : Dirac Mixture Trees for Fast Suboptimal Multi-Dimensional Density Approximation
In : Proceedings of the 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2008)
Address : Seoul, Republic of Korea
Date : August 2008
Abstract
We consider the problem of approximating an
arbitrary multi-dimensional probability density function by
means of a Dirac mixture density. Instead of an optimal
solution based on minimizing a global distance measure between
the true density and its approximation, a fast suboptimal
anytime procedure is proposed, which is based on sequentially
partitioning the state space and component placement by local
optimization. The proposed procedure adaptively covers the
entire state space with a gradually increasing resolution. It
can be efficiently implemented by means of a pre-allocated
tree structure in a straightforward manner. The resulting computational
complexity is linear in the number of components
and linear in the number of dimensions. This allows a large
number of components to be handled, which is especially useful
in high-dimensional state spaces.
Chongning Na, Hui Wang, Dragan Obradovic, Uwe D. Hanebeck,
Fourier Density Approximation for Belief Propagation in Wireless Sensor Networks
Proceedings of the 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2008), pp. 290-295, Seoul, Republic of Korea, August 2008.
PDF URL
Author : Chongning Na, Hui Wang, Dragan Obradovic, Uwe D. Hanebeck
Title : Fourier Density Approximation for Belief Propagation in Wireless Sensor Networks
In : Proceedings of the 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2008)
Address : Seoul, Republic of Korea
Date : August 2008
Abstract
Many distributed inference problems in wireless
sensor networks can be represented by probabilistic graphical
models, where belief propagation, an iterative message passing
algorithm provides a promising solution. In order to make the
algorithm efficient and accurate, messages which carry the
belief information from one node to the others should be
formulated in an appropriate format. This paper presents two
belief propagation algorithms where non-linear and
non-Gaussian beliefs are approximated by Fourier density
approximations, which significantly reduces power
consumptions in the belief computation and transmission. We
use self-localization in wireless sensor networks as an example to
illustrate the performance of this method.
Felix Sawo, Thomas C. Henderson, Christopher Sikorski, Uwe D. Hanebeck,
Sensor Node Localization Methods based on Local Observations of Distributed Natural Phenomena
Proceedings of the 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2008), pp. 301-308, Seoul, Republic of Korea, August 2008.
PDF
Author : Felix Sawo, Thomas C. Henderson, Christopher Sikorski, Uwe D. Hanebeck
Title : Sensor Node Localization Methods based on Local Observations of Distributed Natural Phenomena
In : Proceedings of the 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2008)
Address : Seoul, Republic of Korea
Date : August 2008
Abstract
This paper addresses the model-based localization
of sensor networks based on local observations of a distributed
phenomenon. For the localization process, we propose the
rigorous exploitation of strong mathematical models of distributed
phenomena. By unobtrusively exploiting background
phenomena, the individual sensor nodes can be localized by
only observing its local surrounding without the necessity of
heavy infrastructure. In this paper, we introduce two novel
approaches: (a) the polynomial system localization method
(PSL-method) and (b) the simultaneous reconstruction and
localization method (SRL-method). The first approach (PSLmethod)
is based on restating the mathematical model of the
distributed phenomenon in terms of a polynomial system.
These equations depend on both the state of the phenomenon
and the node locations. Solving the system of polynomials
for each individual sensor node directly leads to the desired
locations. The second approach (SRL-method) basically regards
the localization problem as a simultaneous state and parameter
estimation problem with the node locations as parameters. By
this means, the distributed phenomenon is reconstructed and
the individual nodes are localized in a simultaneous fashion.
In addition, within this framework the uncertainties in the
mathematical model and the measurements are considered.
The performance of the two different localization approaches
is demonstrated by means of simulation results.
Winner Best Paper Award Certificate (PDF)
Gregor F. Schwarzenberg, Uwe Mayer, Nicole V. Ruiter, Uwe D. Hanebeck,
3D Reflectivity Reconstruction by Means of Spatially Distributed Kalman Filters
Proceedings of the 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2008), pp. 384-391, Seoul, Republic of Korea, August 2008.
PDF
Author : Gregor F. Schwarzenberg, Uwe Mayer, Nicole V. Ruiter, Uwe D. Hanebeck
Title : 3D Reflectivity Reconstruction by Means of Spatially Distributed Kalman Filters
In : Proceedings of the 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2008)
Address : Seoul, Republic of Korea
Date : August 2008
Abstract
In seismic, radar, and sonar imaging the exact determination
of the reflectivity distribution is usually intractable
so that approximations have to be applied. A method called
synthetic aperture focusing technique (SAFT) is typically used
for such applications as it provides a fast and simple method to
reconstruct (3D) images. Nevertheless, this approach has several
drawbacks such as causing image artifacts as well as offering
no possibility to model system-specific uncertainties.
In this paper, a statistical approach is derived, which models
the region of interest as a probability density function (PDF)
representing spatial reflectivity occurrences. To process the
nonlinear measurements, the exact PDF is approximated by
well-placed Extended Kalman Filters allowing for efficient and
robust data processing.
The performance of the proposed method is demonstrated
for a 3D ultrasound computer tomograph and comparisons are
carried out with the SAFT image reconstruction.
Hui Wang, Andrei Szabo, Joachim Bamberger, Uwe D. Hanebeck,
Simultaneous Multi-Information Fusion and Parameter Estimation for Robust 3-D Indoor Positioning Systems
Proceedings of the 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2008), pp. 445-450, Seoul, Republic of Korea, August 2008.
PDF URL
Author : Hui Wang, Andrei Szabo, Joachim Bamberger, Uwe D. Hanebeck
Title : Simultaneous Multi-Information Fusion and Parameter Estimation for Robust 3-D Indoor Positioning Systems
In : Proceedings of the 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2008)
Address : Seoul, Republic of Korea
Date : August 2008
Abstract
Typical WLAN based indoor positioning systems
take the received signal strength (RSS) as the major information
source. Due to the complicated indoor environment, the RSS
measurements are hard to model and too noisy to achieve a
satisfactory 3-D accuracy in multi-floor scenarios. To enhance
the performance of WLAN positioning systems, extra information
sources could be integrated. In this paper, a Bayesian
framework is applied to fuse multi-information sources and
estimate the spatial and time varying parameters simultaneously
and adaptively. An application of this framework, which
fuses pressure measurements, a topological building map with
RSS measurements, and simultaneously estimates the pressure
sensor bias, is investigated. Our experiments indicate that the
localization performance is more accurate and robust by using
our approach.
Florian Weissel, Thomas Schreiter, Marco F. Huber, Uwe D. Hanebeck,
Stochastic Model Predictive Control of Time-Variant Nonlinear Systems with Imperfect State Information
Proceedings of the 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2008), pp. 40-46, Seoul, Republic of Korea, August 2008.
PDF
Author : Florian Weissel, Thomas Schreiter, Marco F. Huber, Uwe D. Hanebeck
Title : Stochastic Model Predictive Control of Time-Variant Nonlinear Systems with Imperfect State Information
In : Proceedings of the 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2008)
Address : Seoul, Republic of Korea
Date : August 2008
Abstract
In many technical systems, the system state, which
is to be controlled, is not directly accessible, but has to be
estimated from observations. Furthermore, the uncertainties
arising from this procedure are typically neglected in the
controller. To remedy this deficiency, in this paper, we present a
novel approach to stochastic nonlinear model predictive control
(NMPC) for heavily noise-affected systems with not directly
accessible, i.e., hidden states, extending the stochastic NMPCframework
presented in [1]. An important property of our novel
method is that, in contrast to classical approaches, time-variant
system and measurement equations as well as time-variant step
rewards can be considered. Extending the techniques from
[1] by introducing virtual future observations and combining
this with a novel tree search algorithm, called probabilistic
branch-and-bound search (PBAB), a solution with a feasible
computational demand of the challenging problem is possible.
Marco F. Huber, Uwe D. Hanebeck,
Gaussian Filter based on Deterministic Sampling for High Quality Nonlinear Estimation
Proceedings of the 17th IFAC World Congress (IFAC 2008), 17, Seoul, Republic of Korea, July 2008.
PDF
Author : Marco F. Huber, Uwe D. Hanebeck
Title : Gaussian Filter based on Deterministic Sampling for High Quality Nonlinear Estimation
In : Proceedings of the 17th IFAC World Congress (IFAC 2008)
Address : Seoul, Republic of Korea
Date : July 2008
Abstract
In this paper, a Gaussian filter for nonlinear Bayesian estimation is introduced that is
based on a deterministic sample selection scheme. For an effective sample selection, a parametric
density function representation of the sample points is employed, which allows approximating the
cumulative distribution function of the prior Gaussian density. The computationally demanding
parts of the optimization problem formulated for approximation are carried out off-line for
obtaining an efficient filter, whose estimation quality can be altered by adjusting the number
of used sample points. The improved performance of the proposed Gaussian filter compared to
the well-known unscented Kalman fiter is demonstrated by means of two examples.
Felix Sawo, Frederik Beutler, Uwe D. Hanebeck,
Decentralized State Estimation of Distributed Phenomena based on Covariance Bounds
Proceedings of the 17th IFAC World Congress (IFAC 2008), 17, Seoul, Republic of Korea, July 2008.
PDF
Author : Felix Sawo, Frederik Beutler, Uwe D. Hanebeck
Title : Decentralized State Estimation of Distributed Phenomena based on Covariance Bounds
In : Proceedings of the 17th IFAC World Congress (IFAC 2008)
Address : Seoul, Republic of Korea
Date : July 2008
Abstract
This paper addresses the problem of decentralized state estimation of distributed physical
phenomena observed by a sensor network. The centralized approaches are not scalable for large
sensor networks, because all information has to be transmitted to a powerful central processing node
requiring an extensive amount of communication bandwidth and a lot of processing power. Thus, for a
decentralized reconstruction of distributed phenomena, we propose a novel methodology consisting of
three steps: (a) conversion of the distributed phenomenon into a lumped-parameter system description,
(b) decomposition of the resulting system in order to map the description to the actual sensor network,
and (c) decomposition of the density representation leading to a decentralized estimation approach. The
main problem of a decentralized approach is that due to the propagation of local information through the
network, unknown correlations are caused. This fact needs to be considered during the reconstruction
process in order to get correct and consistent estimation results. For that reason, we employ a robust
estimator (based on Covariance Bounds) for the local reconstruction update on each sensor node. By this
means, the individual sensor nodes are able to estimate the local state of the distributed phenomenon
using local estimates obtained and communicated by adjacent nodes only. The information about their
correlations is not stored in the sensor network.
Florian Weissel, Marco F. Huber, Dietrich Brunn, Uwe D. Hanebeck,
Stochastic Optimal Control based on Value-Function Approximation using Sinc Interpolation
Proceedings of the 17th IFAC World Congress (IFAC 2008), 17, Seoul, Republic of Korea, July 2008.
PDF
Author : Florian Weissel, Marco F. Huber, Dietrich Brunn, Uwe D. Hanebeck
Title : Stochastic Optimal Control based on Value-Function Approximation using Sinc Interpolation
In : Proceedings of the 17th IFAC World Congress (IFAC 2008)
Address : Seoul, Republic of Korea
Date : July 2008
Abstract
An effcient approach for solving stochastic optimal control problems is to employ
dynamic programming (DP). For continuous-valued nonlinear systems, the corresponding
DP recursion generally cannot be solved in closed form. Thus, a typical approach is to
discretize the DP value functions in order to be able to carry out the calculation. Especially
for multidimensional systems, either a large number of discretization points is necessary or
the quality of approximation degrades. This problem can be alleviated by interpolating the
discretized value function. In this paper, we present an approach based on optimal low-pass
interpolation employing sinc functions (sine cardinal). For the important case of systems with
Gaussian mixture noise (including the special case of Gaussian noise), we show how the
calculations required for this approach, especially the nontrivial calculation of an expected
value of a Gaussian mixture random variable transformed by a sinc function, can be carried out
analytically. We illustrate the effectiveness of the proposed interpolation scheme by an example
from the field of Stochastic Nonlinear Model Predictive Control (SNMPC).
Marco F. Huber, Uwe D. Hanebeck,
Progressive Gaussian Mixture Reduction
Proceedings of the 11th International Conference on Information Fusion (Fusion 2008), pp. 1-8, Cologne, Germany, July 2008.
PDF
Author : Marco F. Huber, Uwe D. Hanebeck
Title : Progressive Gaussian Mixture Reduction
In : Proceedings of the 11th International Conference on Information Fusion (Fusion 2008)
Address : Cologne, Germany
Date : July 2008
Abstract
For estimation and fusion tasks it is inevitable to
approximate a Gaussian mixture by one with fewer components
to keep the complexity bounded. Appropriate approximations
can be typically generated by exploiting the redundancy in
the shape description of the original mixture. In contrast to
the common approach of successively merging pairs of components
to maintain a desired complexity, the novel Gaussian
mixture reduction algorithm introduced in this paper avoids
to directly reduce the original Gaussian mixture. Instead, an
approximate mixture is generated from scratch by employing
homotopy continuation. This allows starting the approximation
with a single Gaussian, which is constantly adapted to the
progressively incorporated true Gaussian mixture. Whenever a
user-defined bound on the deviation of the approximation cannot
be maintained during the continuation, further components are
added to the approximation. This facilitates significantly reducing
the number of components even for complex Gaussian mixtures.
Marco F. Huber, Uwe D. Hanebeck,
Priority List Sensor Scheduling using Optimal Pruning
Proceedings of the 11th International Conference on Information Fusion (Fusion 2008), pp. 1-8, Cologne, Germany, July 2008.
PDF
Author : Marco F. Huber, Uwe D. Hanebeck
Title : Priority List Sensor Scheduling using Optimal Pruning
In : Proceedings of the 11th International Conference on Information Fusion (Fusion 2008)
Address : Cologne, Germany
Date : July 2008
Abstract
State estimation and reconstruction quality of distributed
phenomena that are monitored by a network of distributed
sensors is strongly affected by communication failures,
which is a problem in real-world sensor networks. In this paper,
we propose a novel sensor scheduling approach named priority
list sensor scheduling (PLSS). This approach facilitates efficient
distributed estimation in sensor networks, even in case of unreliable
communication, by prioritizing the sensor nodes according
to local sensor schedules based on the predicted estimation
error. It is shown that PLSS minimizes the expected estimation
error for arbitrary packet-loss or transmission probabilities.
As prioritizing sensor nodes requires the calculation of several
sensor schedules, a novel pruning algorithm that preserves
optimal schedules is also derived in order to significantly reduce
the computational demand. This is accomplished by exploiting
the monotonicity of the Riccati equation and the information
contribution of individual sensor nodes in combination with a
branch-and-bound technique.
Vesa Klumpp, Felix Sawo, Uwe D. Hanebeck, Dietrich Fränken,
The Sliced Gaussian Mixture Filter for Efficient Nonlinear Estimation
Proceedings of the 11th International Conference on Information Fusion (Fusion 2008), pp. 1-8, Cologne, Germany, July 2008.
PDF
Author : Vesa Klumpp, Felix Sawo, Uwe D. Hanebeck, Dietrich Fränken
Title : The Sliced Gaussian Mixture Filter for Efficient Nonlinear Estimation
In : Proceedings of the 11th International Conference on Information Fusion (Fusion 2008)
Address : Cologne, Germany
Date : July 2008
Abstract
This paper addresses the efficient state estimation
for mixed linear/nonlinear dynamic systems with noisy measurements.
Based on a novel density representation - sliced Gaussian
mixture density - the decomposition into a (conditionally) linear
and nonlinear estimation problem is derived. The systematic
approximation procedure minimizing a certain distance measure
allows the derivation of (close to) optimal and deterministic
estimation results. This leads to high-quality representations of
the measurement-conditioned density of the states and, hence, to
an overall more efficient estimation process. The performance of
the proposed estimator is compared to state-of-the-art estimators,
like the well-known marginalized particle filter.
Benjamin Noack, Vesa Klumpp, Dietrich Brunn, Uwe D. Hanebeck,
Nonlinear Bayesian Estimation with Convex Sets of Probability Densities
Proceedings of the 11th International Conference on Information Fusion (Fusion 2008), pp. 1-8, Cologne, Germany, July 2008.
PDF
Author : Benjamin Noack, Vesa Klumpp, Dietrich Brunn, Uwe D. Hanebeck
Title : Nonlinear Bayesian Estimation with Convex Sets of Probability Densities
In : Proceedings of the 11th International Conference on Information Fusion (Fusion 2008)
Address : Cologne, Germany
Date : July 2008
Abstract
This paper presents a theoretical framework for
Bayesian estimation in the case of imprecisely known probability
density functions. The lack of knowledge about the true density
functions is represented by sets of densities. A formal Bayesian
estimator for these sets is introduced, which is intractable for
infinite sets. To obtain a tractable filter, properties of convex
sets in form of convex polytopes of densities are investigated.
It is shown that pathwise connected sets and their convex hulls
describe the same ignorance. Thus, an exact algorithm is derived,
which only needs to process the hull, delivering tractable results
in the case of a proper parametrization. Since the estimator
delivers a convex hull of densities as output, the theoretical
grounds are laid for deriving efficient Bayesian estimators for
sets of densities. The derived filter is illustrated by means of an
example.
Felix Sawo, Vesa Klumpp, Uwe D. Hanebeck,
Simultaneous State and Parameter Estimation of Distributed-Parameter Physical Systems based on Sliced Gaussian Mixture Filter
Proceedings of the 11th International Conference on Information Fusion (Fusion 2008), pp. 1-8, Cologne, Germany, July 2008.
PDF
Author : Felix Sawo, Vesa Klumpp, Uwe D. Hanebeck
Title : Simultaneous State and Parameter Estimation of Distributed-Parameter Physical Systems based on Sliced Gaussian Mixture Filter
In : Proceedings of the 11th International Conference on Information Fusion (Fusion 2008)
Address : Cologne, Germany
Date : July 2008
Abstract
This paper presents a method for the simultaneous
state and parameter estimation of finite-dimensional models of
distributed systems monitored by a sensor network. In the
first step, the distributed system is spatially and temporally
decomposed leading to a linear finite-dimensional model in state
space form. The main challenge is that the simultaneous state and
parameter estimation of such systems leads to a high-dimensional
nonlinear problem. Thanks to the linear substructure contained
in the resulting finite-dimensional model, the development of an
overall more efficient estimation process is possible. Therefore,
in the second step, we propose the application of a novel density
representation - sliced Gaussian mixture density - in order to
decompose the estimation problem into a (conditionally) linear
and a nonlinear problem. The systematic approximation procedure
minimizing a certain distance measure allows the derivation
of (close to) optimal and deterministic results. The proposed
estimation process provides novel prospects in sensor network
applications. The performance is demonstrated by means of
simulation results.
Hui Wang, Andrei Szabo, Joachim Bamberger, Dietrich Brunn, Uwe D. Hanebeck,
Performances Comparison of Nonlinear Filters for Indoor WLAN Positioning
Proceedings of the 11th International Conference on Information Fusion (Fusion 2008), pp. 1-7, Cologne, Germany, July 2008.
PDF
Author : Hui Wang, Andrei Szabo, Joachim Bamberger, Dietrich Brunn, Uwe D. Hanebeck
Title : Performances Comparison of Nonlinear Filters for Indoor WLAN Positioning
In : Proceedings of the 11th International Conference on Information Fusion (Fusion 2008)
Address : Cologne, Germany
Date : July 2008
Abstract
Indoor WLAN positioning should be modeled as a nonlinear
and non-Gaussian dynamic system due to the complex indoor environment,
radio propagation and motion behaviour. The aim of this paper is to
analyze different filtering strategies for real life indoor WLAN
positioning systems. The performance criteria for the comparison are
the mean of localization errors and computational complexity.
Three nonlinear filters are analyzed: Fourier density approximation (FF),
particle filter (PF) and grid-based filter (GF), which are representatives for
deterministic and random density approximation approaches.
Our experimental results help to choose the appropriate filtering
techniques under different resource limitations.
Felix Sawo, Kathrin Roberts, Uwe D. Hanebeck,
Model-Based Reconstruction of Distributed Phenomena Using Discretized Representations of Partial Differential Equations
Informatics in Control, Automation and Robotics, Selected Papers from ICINCO 2006, Series: Lecture Notes in Electrical Engineering, 15, Springer, 2008.
URL
Author : Felix Sawo, Kathrin Roberts, Uwe D. Hanebeck
Title : Model-Based Reconstruction of Distributed Phenomena Using Discretized Representations of Partial Differential Equations
In : Informatics in Control, Automation and Robotics, Selected Papers from ICINCO 2006, Series: Lecture Notes in Electrical Engineering
Address :
Date : 2008
Abstract
This article addresses the model-based reconstruction and prediction of distributed phenomena
characterized by partial differential equations, which are monitored by sensor networks. The novelty of the
proposed reconstruction method is the systematic approach and the integrated treatment of uncertainties,
which occur in the physical model and arise naturally from noisy measurements. By this means it is possible
not only to reconstruct the entire phenomenon, even at non-measurement points, but also to reconstruct the
complete density function of the state characterizing the distributed phenomenon. In the first step, the
partial differential equation, i.e., distributed-parameter system, is spatially and temporally decomposed
leading to a finite-dimensional state space form. In the next step, the state of the resulting lumped-parameter
system, which provides an approximation of the solution of the underlying partial differential equation,
is dynamically estimated under consideration of uncertainties. By using the estimation results, several
additional tasks can be achieved by the sensor network, e.g. optimal sensor placement, optimal scheduling,
model improvement, and system identification. The performance of the proposed model-based reconstruction
method is demonstrated by means of simulations.

Publikationen aus dem Jahr 2007

Uwe D. Hanebeck, Oliver C. Schrempf,
Greedy Algorithms for Dirac Mixture Approximation of Arbitrary Probability Density Functions
Proceedings of the 2007 IEEE Conference on Decision and Control (CDC 2007), pp. 3065-3071, New Orleans, Louisiana, USA, December 2007.
PDF
Author : Uwe D. Hanebeck, Oliver C. Schrempf
Title : Greedy Algorithms for Dirac Mixture Approximation of Arbitrary Probability Density Functions
In : Proceedings of the 2007 IEEE Conference on Decision and Control (CDC 2007)
Address : New Orleans, Louisiana, USA
Date : December 2007
Abstract
Greedy procedures for suboptimal Dirac mixture approximation of an
arbitrary probability density function are proposed, which approach
the desired density by sequentially adding one component at a time.
Similar to the batch solutions proposed earlier, a distance measure
between the corresponding cumulative distributions is minimized by
selecting the corresponding density parameters. This is due to the
fact, that a distance between the densities is typically not well
defined for Dirac mixtures. This paper focuses on the Cramer-von
Mises distance, a weighted integral quadratic distance measure between
the true distribution and its approximation. In contrast to the batch
solutions, the computational complexity is much lower and grows only
linearly with the number of components. Computational savings are
even greater, when the required number of components, e.g., the minimum
number of components for achieving a given quality measure, is not
a priori known and must be searched for as well. The performance
of the proposed sequential approximation approach is compared to
that of the optimal batch solution.
Florian Weissel, Marco F. Huber, Uwe D. Hanebeck,
A Nonlinear Model Predictive Control Framework Approximating Noise Corrupted Systems with Hybrid Transition Densities
Proceedings of the 2007 IEEE Conference on Decision and Control (CDC 2007), pp. 3661-3666, New Orleans, Louisiana, USA, December 2007.
PDF
Author : Florian Weissel, Marco F. Huber, Uwe D. Hanebeck
Title : A Nonlinear Model Predictive Control Framework Approximating Noise Corrupted Systems with Hybrid Transition Densities
In : Proceedings of the 2007 IEEE Conference on Decision and Control (CDC 2007)
Address : New Orleans, Louisiana, USA
Date : December 2007
Abstract
In this paper, a framework for Nonlinear Model Predictive Control
(NMPC) for heavily noise-affected systems is presented. Within this
framework, the noise influence, which originates from uncertainties
during model identification or measurement, is explicitly considered.
This leads to a significant increase in the control quality. One
part of the proposed framework is the efficient state prediction,
which is necessary for NMPC. It is based on transition density approximation
by hybrid transition densities, which allows efficient closed-form
state prediction of time-variant nonlinear systems with continuous
state spaces in discrete time. Another part of the framework is a
versatile value function representation using Gaussian mixtures,
Dirac mixtures, and even a combination of both. Based on these methods,
an efficient closed-form algorithm for calculating an approximate
value function of the NMPC optimal control problem employing dynamic
programming is presented. Thus, also very long optimization horizons
can be used and furthermore it is possible to calculate the value
function off-line, which reduces the on-line computational burden
significantly. The capabilities of the framework and especially the
benefits that can be gained by incorporating the noise in the controller
are illustrated by the example of a miniature walking robot following
a given path.
Oliver C. Schrempf, David Albrecht, Uwe D. Hanebeck,
Tractable Probabilistic Models for Intention Recognition Based on Expert Knowledge
Proceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2007), pp. 1429-1434, San Diego, California, USA, November 2007.
PDF
Author : Oliver C. Schrempf, David Albrecht, Uwe D. Hanebeck
Title : Tractable Probabilistic Models for Intention Recognition Based on Expert Knowledge
In : Proceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2007)
Address : San Diego, California, USA
Date : November 2007
Abstract
Intention recognition is an important topic in human-robot cooperation
that can be tackled using probabilistic model-based methods. A popular
instance of such methods are Bayesian networks where the dependencies
between random variables are modeled by means of a directed graph.
Bayesian networks are very efficient for treating networks with conditionally
independent parts. Unfortunately, such independence sometimes has
to be constructed by introducing so called hidden variables with
an intractably large state space. An example are human actions which
depend on human intentions and on other human actions. Our goal in
this paper is to find models for intention-action mapping with a
reduced state space in order to allow for tractable on-line evaluation.
We present a systematic derivation of the reduced model and experimental
results of recognizing the intention of a real human in a virtual
environment.
Florian Weissel, Marco F. Huber, Uwe D. Hanebeck,
Test-Environment based on a Team of Miniature Walking Robots for Evaluation of Collaborative Control Methods
Proceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2007), pp. 2474-2479, San Diego, California, USA, November 2007.
PDF
Author : Florian Weissel, Marco F. Huber, Uwe D. Hanebeck
Title : Test-Environment based on a Team of Miniature Walking Robots for Evaluation of Collaborative Control Methods
In : Proceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2007)
Address : San Diego, California, USA
Date : November 2007
Abstract
For the collaborative control of a team of robots, a set of well-suited
high-level control algorithms, especially for path planning and measurement
scheduling, is essential. The quality of these control algorithms
can be significantly increased by considering uncertainties that
arise, e.g. from noisy measurements or system model abstraction,
by incorporating stochastic filters into the control. To develop
these kinds of algorithms and to prove their effectiveness, obviously
real-world experiments with real world uncertainties are mandatory.
Therefore, a test-environment for evaluating algorithms for collaborative
control of a team of robots is presented. This test-environment is
founded on miniature walking robots with six degrees of freedom.
Their novel locomotion concept not only allows them to move in a
wide variety of different motion patterns far beyond the possibilities
of traditionally employed wheel-based robots, but also to handle
real-world conditions like uneven ground or small obstacles. These
robots are embedded in a modular test-environment, comprising infrastructure
and simulation modules as well as a high-level control module with
submodules for pose estimation, path planning, and measurement scheduling.
The interaction of the individual modules of the introduced test-environment
is illustrated by an experiment from the field of cooperative localization
with focus on measurement scheduling, where the robots that perform
distance measurements are selected based on a novel criterion, the
normalized mutual Mahalanobis distance.
Marco F. Huber, Eric Stiegeler, Uwe D. Hanebeck,
On Sensor Scheduling in Case of Unreliable Communication
INFORMATIK 2007 - the 37th Annual Conference of the Gesellschaft für Informatik e.V. (GI), 3rd German Workshop Sensor Data Fusion: Trends, Solutions, Applications (SDF 2007), pp. 90-94, Bremen, Germany, September 2007.
PDF
Author : Marco F. Huber, Eric Stiegeler, Uwe D. Hanebeck
Title : On Sensor Scheduling in Case of Unreliable Communication
In : INFORMATIK 2007 - the 37th Annual Conference of the Gesellschaft für Informatik e.V. (GI), 3rd German Workshop Sensor Data Fusion: Trends, Solutions, Applications (SDF 2007)
Address : Bremen, Germany
Date : September 2007
Abstract
This paper deals with the linear discrete-time sensor scheduling problem
in unreliable communication networks. In case of the common assumption
of an error-free communication, the sensor scheduling problem, where
one sensor from a sensor network is selected for measuring at a specific
time instant so that the estimation errors are minimized, can be
solved off-line by extensive tree search. For the more realistic
scenario, where communication is unreliable, a scheduling approach
using a prioritization list for the sensors is proposed that leads
to a minimization of the estimation error by selecting the most beneficial
sensor on-line. To lower the computational demand for the priority
list calculation, a novel optimal pruning approach is introduced.
Marco F. Huber, Dietrich Brunn, Uwe D. Hanebeck,
Efficient Nonlinear Measurement Updating based on Gaussian Mixture Approximation of Conditional Densities
Proceedings of the 2007 American Control Conference (ACC 2007), pp. 4425-4430, New York, New York, USA, July 2007.
PDF
Author : Marco F. Huber, Dietrich Brunn, Uwe D. Hanebeck
Title : Efficient Nonlinear Measurement Updating based on Gaussian Mixture Approximation of Conditional Densities
In : Proceedings of the 2007 American Control Conference (ACC 2007)
Address : New York, New York, USA
Date : July 2007
Abstract
Filtering or measurement updating for nonlinear stochastic dynamic
systems requires approximate calculations, since an exact solution
is impossible to obtain in general. We propose a Gaussian mixture
approximation of the conditional density, which allows performing
measurement updating in closed form. The conditional density is a
probabilistic representation of the nonlinear system and depends
on the random variable of the measurement given the system state.
Unlike the likelihood, the conditional density is independent of
actual measurements, which permits determining its approximation
off-line. By treating the approximation task as an optimization problem,
we use progressive processing to achieve high quality results. Once
having calculated the conditional density, the likelihood can be
determined on-line, which, in turn, offers an efficient approximate
filter step. As result, a Gaussian mixture representation of the
posterior density is obtained. The exponential growth of Gaussian
mixture components resulting from repeated filtering is avoided implicitly
by the prediction step using the proposed techniques.
Oliver C. Schrempf, Uwe D. Hanebeck,
Recursive Prediction of Stochastic Nonlinear Systems Based on Optimal Dirac Mixture Approximations
Proceedings of the 2007 American Control Conference (ACC 2007), pp. 1768-1774, New York, New York, USA, July 2007.
PDF
Author : Oliver C. Schrempf, Uwe D. Hanebeck
Title : Recursive Prediction of Stochastic Nonlinear Systems Based on Optimal Dirac Mixture Approximations
In : Proceedings of the 2007 American Control Conference (ACC 2007)
Address : New York, New York, USA
Date : July 2007
Abstract
This paper introduces a new approach to the recursive propagation
of probability density functions through discrete-time stochastic
nonlinear dynamic systems. An efficient recursive procedure is proposed
that is based on the optimal approximation of the posterior densities
after each prediction step by means of Dirac mixtures. The parameters
of the individual components are selected by systematically minimizing
a suitable distance measure in such a way that the future evolution
of the approximate densities is as close to the exact densities as
possible.
Florian Weissel, Marco F. Huber, Uwe D. Hanebeck,
Efficient Control of Nonlinear Noise-Corrupted Systems Using a Novel Model Predictive Control Framework
Proceedings of the 2007 American Control Conference (ACC 2007), pp. 3751-3756, New York, New York, USA, July 2007.
PDF
Author : Florian Weissel, Marco F. Huber, Uwe D. Hanebeck
Title : Efficient Control of Nonlinear Noise-Corrupted Systems Using a Novel Model Predictive Control Framework
In : Proceedings of the 2007 American Control Conference (ACC 2007)
Address : New York, New York, USA
Date : July 2007
Abstract
Model identification and measurement acquisition is always to some
degree uncertain. Therefore, a framework for Nonlinear Model Predictive
Control (NMPC) is proposed that explicitly considers the noise influence
on nonlinear dynamic systems with continuous state spaces and a finite
set of control inputs in order to significantly increase the control
quality. Integral parts of NMPC are the prediction of system states
over a finite horizon as well as the problem specific modeling of
reward functions. For achieving an efficient and also accurate state
prediction, the introduced framework uses transition densities approximated
by means of axis-aligned Gaussian mixtures. The representation power
of Gaussian mixtures is also used to model versatile reward functions.
Thus, together with the prediction technique a closed-form calculation
of the optimization problems arising from NMPC is possible. Additionally,
an efficient algorithm for calculating an approximate value function
of the corresponding optimal control problem employing dynamic programming
is presented. Thus, the value function can be calculated off-line,
which reduces the on-line computational burden significantly and
also permits the use of long optimization horizons. The capabilities
of the framework and especially the benefits that can be gained by
incorporating the noise in the controller are illustrated by the
example of a two-wheeled differential-drive mobile robot following
a given path.
Marc P. Deisenroth, Florian Weissel, Toshiyuki Ohtsuka, Uwe D. Hanebeck,
Online-Computation Approach to Optimal Control of Noise-Affected Nonlinear Systems with Continuous State and Control Spaces
Proceedings of the 2007 European Control Conference (ECC 2007), Kos, Greece, July 2007.
PDF
Author : Marc P. Deisenroth, Florian Weissel, Toshiyuki Ohtsuka, Uwe D. Hanebeck
Title : Online-Computation Approach to Optimal Control of Noise-Affected Nonlinear Systems with Continuous State and Control Spaces
In : Proceedings of the 2007 European Control Conference (ECC 2007)
Address : Kos, Greece
Date : July 2007
Abstract
A novel online-computation approach to optimal control of nonlinear,
noise-affected systems with continuous state and control spaces is
presented. In the proposed algorithm, system noise is explicitly
incorporated into the control decision. This leads to superior results
compared to state-of-the-art nonlinear controllers that neglect this
influence. The solution of an optimal nonlinear controller for a
corresponding deterministic system is employed to find a meaningful
state space restriction. This restriction is obtained by means of
approximate state prediction using the noisy system equation. Within
this constrained state space, an optimal closed-loop solution for
a finite decisionmaking horizon (prediction horizon) is determined
within an adaptively restricted optimization space. Interleaving
stochastic dynamic programming and value function approximation yields
a solution to the considered optimal control problem. The enhanced
performance of the proposed discrete-time controller is illustrated
by means of a scalar example system. Nonlinear model predictive control
is applied to address approximate treatment of infinite-horizon problems
by the finite-horizon controller.
Anne Hanselmann, Oliver C. Schrempf, Uwe D. Hanebeck,
Optimal Parametric Density Estimation by Minimizing an Analytic Distance Measure
Proceedings of the 10th International Conference on Information Fusion (Fusion 2007), Quebec, Canada, July 2007.
PDF
Author : Anne Hanselmann, Oliver C. Schrempf, Uwe D. Hanebeck
Title : Optimal Parametric Density Estimation by Minimizing an Analytic Distance Measure
In : Proceedings of the 10th International Conference on Information Fusion (Fusion 2007)
Address : Quebec, Canada
Date : July 2007
Abstract
In this paper, we present a novel approach to parametric density estimation
from given samples. The samples are treated as a parametric density
function by means of a Dirac mixture, which allows for applying analytic
optimization techniques. The method is based on minimizing a distance
measure between the integral of the approximation function and the
empirical cumulative distribution function (EDF) of the given samples,
where the EDF is represented by the integral of the Dirac mixture.
Since this minimization problem cannot be solved directly in general,
a progression technique is applied. Increased performance of the
approach in comparison to iterative maximum likelihood approaches
is shown in simulations.
Marco F. Huber, Uwe D. Hanebeck,
The Hybrid Density Filter for Nonlinear Estimation based on Hybrid Conditional Density Approximation
Proceedings of the 10th International Conference on Information Fusion (Fusion 2007), Quebec, Canada, July 2007.
PDF
Author : Marco F. Huber, Uwe D. Hanebeck
Title : The Hybrid Density Filter for Nonlinear Estimation based on Hybrid Conditional Density Approximation
In : Proceedings of the 10th International Conference on Information Fusion (Fusion 2007)
Address : Quebec, Canada
Date : July 2007
Abstract
In nonlinear Bayesian estimation it is generally inevitable to incorporate
approximate descriptions of the exact estimation algorithm. There
are two possible ways to involve approximations: Approximating the
nonlinear stochastic system model or approximating the prior probability
density function. The key idea of the introduced novel estimator
called Hybrid Density Filter relies on approximating the nonlinear
system, thus approximating conditional densities. These densities
nonlinearly relate the current system state to the future system
state at predictions or to potential measurements at measurement
updates. A hybrid density consisting of both Dirac delta functions
and Gaussian densities is used for an optimal approximation. This
paper addresses the optimization problem for treating the conditional
density approximation. Furthermore, efficient estimation algorithms
are derived based upon the special structure of the hybrid density,
which yield a Gaussian mixture representation of the system state's
density.
Felix Sawo, Marco F. Huber, Uwe D. Hanebeck,
Parameter Identification and Reconstruction for Distributed Phenomena Based on Hybrid Density Filter
Proceedings of the 10th International Conference on Information Fusion (Fusion 2007), Quebec, Canada, July 2007.
PDF
Author : Felix Sawo, Marco F. Huber, Uwe D. Hanebeck
Title : Parameter Identification and Reconstruction for Distributed Phenomena Based on Hybrid Density Filter
In : Proceedings of the 10th International Conference on Information Fusion (Fusion 2007)
Address : Quebec, Canada
Date : July 2007
Abstract
This paper addresses the problem of model-based reconstruction and
parameter identification of distributed phenomena characterized by
partial differential equations. The novelty of the proposed method
is the systematic approach and the integrated treatment of uncertainties,
which naturally occur in the physical system and arise from noisy
measurements. The main challenge of accurate reconstruction is that
model parameters, i.e., diffusion coefficients, of the physical model
are not known in advance and usually need to be identified. Generally,
the problem of parameter identification leads to a nonlinear estimation
problem. Hence, a novel efficient recursive procedure is employed.
Unlike other estimators, the so-called Hybrid Density Filter not
only assures accurate estimation results for nonlinear systems, but
also offers an efficient processing. By this means it is possible
to reconstruct and identify distributed phenomena monitored by autonomous
wireless sensor networks. The performance of the proposed estimation
method is demonstrated by means of simulations.
Hui Wang, Henning Lenz, Andrei Szabo, Joachim Bamberger, Uwe D. Hanebeck,
Enhancing the Map Usage for Indoor Location-Aware Systems
International Conference on Human-Computer Interaction (HCI 2007), Peking, China, July 2007.
PDF
Author : Hui Wang, Henning Lenz, Andrei Szabo, Joachim Bamberger, Uwe D. Hanebeck
Title : Enhancing the Map Usage for Indoor Location-Aware Systems
In : International Conference on Human-Computer Interaction (HCI 2007)
Address : Peking, China
Date : July 2007
Abstract
Location-aware systems are receiving more and more interest in both
academia and industry due to their promising prospective in a broad
category of so-called Location-Based-Services (LBS). The map interface
plays a crucial role in the location-aware systems, especially for
indoor scenarios. This paper addresses the usage of map information
in a Wireless LAN (WLAN)-based indoor navigation system. We describe
the benefit of using maNMp information in multiple algorithms of
the system, including radio-map generation, tracking, semantic positioning
and navigation. Then we discuss how to represent or model the indoor
map to fulfill the requirements of intelligent algorithms. We believe
that a vector-based multi-layer representation is the best choice
for indoor location-aware system.
Kathrin Roberts, Uwe D. Hanebeck,
Motion Estimation and Reconstruction of a Heart Surface by Means of 2D-/3D- Membrane Models
Proceedings of 21st International Congress and Exhibition on Computer Assisted Radiology and Surgery (CARS 2007), pp. 243-245, Berlin, Germany, June 2007.
PDF
Author : Kathrin Roberts, Uwe D. Hanebeck
Title : Motion Estimation and Reconstruction of a Heart Surface by Means of 2D-/3D- Membrane Models
In : Proceedings of 21st International Congress and Exhibition on Computer Assisted Radiology and Surgery (CARS 2007)
Address : Berlin, Germany
Date : June 2007
Abstract
In order to assist surgeons during minimally invasive interventions
on the beating heart, it would be helpful to develop a robotic surgery
system, which synchronizes the instruments with the heart surface,
so that their positions do not change relative to the point of interest
(POI). The synchronization of the robotic manipulators requires an
estimation of the heart surface motion. In this paper, a modelbased
motion estimation of the heart surface is presented. The motion of
a partition of the heart surface is modelled by means of a thin or
thick vibrating membrane in order to represent the epicardial surface
or the connected epicard and myocard. The membrane motion is described
by means of a system of coupled linear partial differential equations
(PDEs), whose 3D-input function is assumed to be known. After spatial
discretization of the PDE solution space by the Finite Spectral Element
Method, a bank of lumped systems is obtained. A Kalman filter is
used to estimate the state of the lumped systems by incorporating
noisy measurements of the heart surface. Measurements can be the
position or velocity of sonomicrometry-based sensors or of certain
landmarks, which are tracked by optical sensors. With the model-based
estimation it is possible to reconstruct the entire partition of
the heart surface even at non-measurement points and thus at each
POI.
Oliver C. Schrempf, Uwe D. Hanebeck,
A State Estimator for Nonlinear Stochastic Systems Based on Dirac Mixture Approximations
Proceedings of the 4th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2007), SPSMC:54-61, Angers, France, May 2007.
PDF
Author : Oliver C. Schrempf, Uwe D. Hanebeck
Title : A State Estimator for Nonlinear Stochastic Systems Based on Dirac Mixture Approximations
In : Proceedings of the 4th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2007)
Address : Angers, France
Date : May 2007
Abstract
This paper presents a filter approach for estimating the state of
nonlinear dynamic systems based on recursive approximation of posterior
densities by means of Dirac mixture functions. The filter consists
of a prediction step and a filter step. The approximation approach
is based on a systematic minimization of a distance measure and is
hence optimal and deterministic. In contrast to non-deterministic
methods we are able to determine the optimal number of components
in the Dirac mixture. A further benefit of the proposed approach
is the consideration of measurements during the approximation process
in order to avoid parameter degradation.
Florian Weissel, Marco F. Huber, Uwe D. Hanebeck,
A Closed-Form Model Predictive Control Framework for Nonlinear Noise-Corrupted Systems
Proceedings of the 4th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2007), SPSMC:62-69, Angers, France, May 2007.
PDF
Author : Florian Weissel, Marco F. Huber, Uwe D. Hanebeck
Title : A Closed-Form Model Predictive Control Framework for Nonlinear Noise-Corrupted Systems
In : Proceedings of the 4th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2007)
Address : Angers, France
Date : May 2007
Abstract
In this paper, a framework for Nonlinear Model Predictive Control
(NMPC) that explicitly incorporates the noise influence on systems
with continuous state spaces is introduced. By the incorporation
of noise, which results from uncertainties during model identification
and the measurement process, the quality of control can be significantly
increased. Since NMPC requires the prediction of system states over
a certain horizon, an efficient state prediction technique for nonlinear
noise-affected systems is required. This is achieved by using transition
densities approximated by axis-aligned Gaussian mixtures together
with methods to reduce the computational burden. A versatile cost
function representation also employing Gaussian mixtures provides
an increased freedom of modeling. Combining the prediction technique
with this value function representation allows closed-form calculation
of the necessary optimization problems arising from NMPC. The capabilities
of the framework and especially the benefits that can be gained by
considering the noise in the controller are illustrated by the example
of a mobile robot following a given path.
Thomas Bader, Alexander Wiedemann, Kathrin Roberts, Uwe D. Hanebeck,
Model-based Motion Estimation of Elastic Surfaces for Minimally Invasive Cardiac Surgery
Proceedings of the 2007 IEEE International Conference on Robotics and Automation (ICRA 2007), pp. 2261-2266, Rome, Italy, April 2007.
PDF
Author : Thomas Bader, Alexander Wiedemann, Kathrin Roberts, Uwe D. Hanebeck
Title : Model-based Motion Estimation of Elastic Surfaces for Minimally Invasive Cardiac Surgery
In : Proceedings of the 2007 IEEE International Conference on Robotics and Automation (ICRA 2007)
Address : Rome, Italy
Date : April 2007
Abstract
In order to assist surgeons during surgery on moving organs, e.g.
minimally invasive beating heart bypass surgery, a master-slave system
which synchronizes surgical instruments with the organ's motion is
desired. This synchronization requires reliable estimation of the
organ's motion. In this paper, we present a new approach to motion
estimation based on a state motion model for a partition of the heart's
surface. Its motion behavior is described by a partial differential
equation whose input function is assumed to be periodic. An estimator
is used on one hand to predict future model states based on reconstruction
of the input function and on the other hand to incorporate noisy
spatially discrete measurements in order to improve state estimation.
The model-based motion estimation is evaluated using a simple heart
simulator. Measurements are obtained by reconstructing 3D position
of markers on a pulsating membrane by means of a stereo camera system.
Marco F. Huber, Uwe D. Hanebeck,
Hybrid Transition Density Approximation for Efficient Recursive Prediction of Nonlinear Dynamic Systems
International Conference on Information Processing in Sensor Networks (IPSN 2007), pp. 283-292, Cambridge, Massachusetts, USA, April 2007.
PDF
Author : Marco F. Huber, Uwe D. Hanebeck
Title : Hybrid Transition Density Approximation for Efficient Recursive Prediction of Nonlinear Dynamic Systems
In : International Conference on Information Processing in Sensor Networks (IPSN 2007)
Address : Cambridge, Massachusetts, USA
Date : April 2007
Abstract
For several tasks in sensor networks, such as localization, information
fusion, or sensor scheduling, Bayesian estimation is of paramount
importance. Due to the limited computational and memory resources
of the nodes in a sensor network, evaluation of the prediction step
of the Bayesian estimator has to be performed very efficiently. An
exact and closed-form representation of the predicted probability
density function of the system state is typically impossible to obtain,
since exactly solving the prediction step for nonlinear discrete-time
dynamic systems in closed form is unfeasible. Assuming additive noise,
we propose an accurate approximation of the predicted density, that
can be calculated efficiently by optimally approximating the transition
density using a hybrid density. A hybrid density consists of two
different density types: Dirac delta functions that cover the domain
of the current density of the system state, and another density type,
e.g. Gaussian densities, that cover the domain of the predicted density.
The freely selectable, second density type of the hybrid density
depends strongly on the noise affecting the nonlinear system. So,
the proposed approximation framework for nonlinear prediction is
not restricted to a specific noise density. It further allows an
analytical evaluation of the Chapman-Kolmogorov prediction equation
and can be interpreted as a deterministic sampling estimation approach.
In contrast to methods using random sampling like particle filters,
a dramatic reduction in the number of components and a subsequent
decrease in computation time for approximating the predicted density
is gained.
Dietrich Brunn, Felix Sawo, Uwe D. Hanebeck,
Modellbasierte Vermessung verteilter Phänomene und Generierung optimaler Messsequenzen
tm - Technisches Messen, Oldenbourg Verlag, 3:75-90, March 2007.
PDF
Author : Dietrich Brunn, Felix Sawo, Uwe D. Hanebeck
Title : Modellbasierte Vermessung verteilter Phänomene und Generierung optimaler Messsequenzen
In : tm - Technisches Messen, Oldenbourg Verlag
Address :
Date : March 2007
Abstract
Dieser Beitrag befasst sich mit modellbasierten Methoden zur Vermessung
verteilter physikalischer Phänomene. Diese Methoden zeichnen
sich durch eine systematische Behandlung von Unsicherheiten aus,
so dass neben der Rekonstruktion der vollständigen Wahrscheinlichkeitsdichte
der relevanten Größen aus einer geringen Anzahl von zeit-,
orts- und wertdiskreten Messungen auch die Generierung optimaler
Messsequenzen möglich ist. Es wird dargestellt, wie eine Beschreibung
für ein verteilt-parametrisches System in Form einer partiellen
Differentialgleichung, welche einen unendlich-dimensionalen Zustandsraum
beschreibt, in eine konzentriert-parametrische Form konvertiert wird.
Diese kann als Grundlage für den Entwurf klassischer Schätzer,
wie z. B. des Kalman-Filters, dienen. Ferner wird eine Methode zur
Sensoreinsatzplanung vorgestellt, mit der eine optimale Sequenz von
Messparametern bestimmt werden kann, um mit einem minimalen Messaufwand
die Unsicherheit auf ein gewünschtes Maß zu reduzieren. Die
Anwendung dieser Methoden wird an zwei Beispielen, einer Temperaturverteilung
und der Verformung einer Führungsschiene, demonstriert. Zusätzlich
werden die Herausforderungen bei der Behandlung nichtlinearer Systeme
und die Probleme bei der dezentralen Verarbeitung, wie sie typischerweise
beim Einsatz von Sensornetzwerken auftreten, diskutiert.
Hui Wang, Henning Lenz, Andrei Szabo, Joachim Bamberger, Uwe D. Hanebeck,
WLAN-Based Pedestrian Tracking Using Particle Filters and Low-Cost MEMS Sensors
Workshop on Positioning, Navigation and Communication, (WPNC 2007), Hanover, Germany, March 2007.
PDF
Author : Hui Wang, Henning Lenz, Andrei Szabo, Joachim Bamberger, Uwe D. Hanebeck
Title : WLAN-Based Pedestrian Tracking Using Particle Filters and Low-Cost MEMS Sensors
In : Workshop on Positioning, Navigation and Communication, (WPNC 2007)
Address : Hanover, Germany
Date : March 2007
Abstract
Indoor positioning systems based on Wireless LAN
(WLAN) are being widely investigated in academia and industry.
Meanwhile, the emerging low-cost MEMS sensors can also be used
as another independent positioning source. In this paper, we
propose a pedestrian tracking framework based on particle filters,
which extends the typical WLAN-based indoor positioning systems
by integrating low-cost MEMS accelerometer and map
information. Our simulation and real world experiments indicate a
remarkable performance improvement by using this fusion
framework.


Publikationen aus dem Jahr 2006

Dietrich Brunn, Felix Sawo, Uwe D. Hanebeck,
Nonlinear Multidimensional Bayesian Estimation with Fourier Densities
Proceedings of the 2006 IEEE Conference on Decision and Control (CDC 2006), pp. 1303-1308, San Diego, California, USA, December 2006.
PDF
Author : Dietrich Brunn, Felix Sawo, Uwe D. Hanebeck
Title : Nonlinear Multidimensional Bayesian Estimation with Fourier Densities
In : Proceedings of the 2006 IEEE Conference on Decision and Control (CDC 2006)
Address : San Diego, California, USA
Date : December 2006
Abstract
Efficiently implementing nonlinear Bayesian estimators is still an
unsolved problem, especially for the multidimensional case. A trade-off
between estimation quality and demand on computational resources
has to be found. Using multidimensional Fourier series as representation
for probability density functions, so called Fourier densities, is
proposed. To ensure non-negativity, the approximation is performed
indirectly via Psi-densities, of which the absolute square represent
the Fourier density. It is shown that PSI-densities can be determined
using the efficient fast Fourier transform algorithm and their coefficients
have an ordering with respect to the Hellinger metric. Furthermore,
the multidimensional Bayesian estimator based on Fourier Densities
is derived in closed form. That allows an efficient realization of
the Bayesian estimator where the demands on computational resources
are adjustable.
Oliver C. Schrempf, Dietrich Brunn, Uwe D. Hanebeck,
Density Approximation Based on Dirac Mixtures with Regard to Nonlinear Estimation and Filtering
Proceedings of the 2006 IEEE Conference on Decision and Control (CDC 2006), San Diego, California, USA, December 2006.
PDF
Author : Oliver C. Schrempf, Dietrich Brunn, Uwe D. Hanebeck
Title : Density Approximation Based on Dirac Mixtures with Regard to Nonlinear Estimation and Filtering
In : Proceedings of the 2006 IEEE Conference on Decision and Control (CDC 2006)
Address : San Diego, California, USA
Date : December 2006
Abstract
A deterministic procedure for optimal approximation of arbitrary probability
density functions by means of Dirac mixtures with equal weights is
proposed. The optimality of this approximation is guaranteed by minimizing
the distance of the approximation from the true density. For this
purpose a distance measure is required, which is in general not well
defined for Dirac mixtures. Hence, a key contribution is to compare
the corresponding cumulative distribution functions. This paper concentrates
on the simple and intuitive integral quadratic distance measure.
For the special case of a Dirac mixture with equally weighted components,
closed-form solutions for special types of densities like uniform
and Gaussian densities are obtained. Closed-form solution of the
given optimization problem is not possible in general. Hence, another
key contribution is an efficient solution procedure for arbitrary
true densities based on a homotopy continuation approach. In contrast
to standard Monte Carlo techniques like particle filters that are
based on random sampling, the proposed approach is deterministic
and ensures an optimal approximation with respect to a given distance
measure. In addition, the number of required components (particles)
can easily be deduced by application of the proposed distance measure.
The resulting approximations can be used as basis for recursive nonlinear
filtering mechanism alternative to Monte Carlo methods.
Felix Sawo, Dietrich Brunn, Uwe D. Hanebeck,
Parameterized Joint Densities with Gaussian Mixture Marginals and their Potential Use in Nonlinear Robust Estimation
Proceedings of the 2006 IEEE International Conference on Control Applications (CCA 2006), Munich, Germany, October 2006.
PDF
Author : Felix Sawo, Dietrich Brunn, Uwe D. Hanebeck
Title : Parameterized Joint Densities with Gaussian Mixture Marginals and their Potential Use in Nonlinear Robust Estimation
In : Proceedings of the 2006 IEEE International Conference on Control Applications (CCA 2006)
Address : Munich, Germany
Date : October 2006
Abstract
This paper addresses the challenges of the fusion of two random vectors
with imprecisely known stochastic dependency. This problem mainly
occurs in decentralized estimation, e.g. of a distributed phenomenon,
where the stochastic dependencies between the individual states are
not stored. To cope with such problems we propose to exploit parameterized
joint densities with both Gaussian marginals and Gaussian mixture
marginals. Under structural assumptions these parameterized joint
densities contain all information about the stochastic dependencies
between their marginal densities in terms of a generalized correlation
parameter vector xi. The parameterized joint densities are applied
to the prediction step and the measurement step under imprecisely
known correlation leading to a whole family of possible estimation
results. The resulting density functions are characterized by the
generalized correlation parameter vector xi. Once this structure
and the bounds of these parameters are known, it is possible to find
bounding densities containing all possible density functions, i.e.,
conservative estimation results.
Patrick Rößler, Oliver C. Schrempf, Uwe D. Hanebeck,
Stochastic Prediction of Waypoints for Extended-Range Telepresence Applications
2nd International Workshop on Human Centered Robotic Systems (HCRS 2006), pp. 85-89, Munich, Germany, October 2006.
PDF
Author : Patrick Rößler, Oliver C. Schrempf, Uwe D. Hanebeck
Title : Stochastic Prediction of Waypoints for Extended-Range Telepresence Applications
In : 2nd International Workshop on Human Centered Robotic Systems (HCRS 2006)
Address : Munich, Germany
Date : October 2006
Abstract
The Motion Compression framework for extended range telepresence
applications consists of three functional modules:
path prediction, path transformation, and user guidance. This
paper presents a new path prediction module for known environments
that exploits the property, that humans typically
walk on straight paths toward discrete goal objects. In order
to estimate the user's goal object out of a set of possible goals,
we derived a Bayesian filter that gives this discrete estimate
based on continuous measurements of the user's head pose.
Andreas J. Schmid, Oliver C. Schrempf, Heinz Wörn, Uwe D. Hanebeck,
Towards Intuitive Human-Robot Cooperation
2nd International Workshop on Human Centered Robotic Systems (HCRS 2006), pp. 7-12, Munich, Germany, October 2006.
PDF
Author : Andreas J. Schmid, Oliver C. Schrempf, Heinz Wörn, Uwe D. Hanebeck
Title : Towards Intuitive Human-Robot Cooperation
In : 2nd International Workshop on Human Centered Robotic Systems (HCRS 2006)
Address : Munich, Germany
Date : October 2006
Abstract
Human-robot cooperation calls for the treatment of human-machine
communication channels, especially if humanoid robots
are involved. In this paper, we consider implicit nonverbal
channels given by recognizing the partner's intention
and proactive execution of tasks. We propose a method that
keeps the human in the loop and allows for the systematic reduction
of uncertainty inherent in implicit cooperation. We
present a benchmark scenario as well as preliminary implementation
results.
Patrick Rößler, Uwe D. Hanebeck,
Simultaneous Motion Compression for Multi-User Extended Range Telepresence
Proceedings of the 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2006), pp. 5189-5194, Beijing, China, October 2006.
PDF
Author : Patrick Rößler, Uwe D. Hanebeck
Title : Simultaneous Motion Compression for Multi-User Extended Range Telepresence
In : Proceedings of the 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2006)
Address : Beijing, China
Date : October 2006
Abstract
Extended range telepresence allows a human user
to intuitively teleoperate a mobile robot through arbitrarily large
remote environments by natural walking. In order to give the user
the possibility to navigate the robot through an arbitrarily large
remote environments, while his own environment is of limited
size, Motion Compression is used. The Motion Compression
framework provides a nonlinear transformation between the
user's path and the robot's path, which preserves path length and
turning angles. There is, however, a difference in path curvature,
which is minimized in order to guarantee a high degree of
immersion.
A major drawback of the current system is its inability to
deal with non-convex time-variant environments or environments
shared by multiple users. This paper presents a systematic
approach to extending Motion Compression to non-convex environments.
This solution will then be used to cover the multi-user
case.
Dietrich Brunn, Felix Sawo, Uwe D. Hanebeck,
Informationsfusion für verteilte Systeme
Informationsfusion in der Mess- und Sensortechnik, pp. 75-90, Universitätsverlag Karlsruhe, September 2006.
PDF
Author : Dietrich Brunn, Felix Sawo, Uwe D. Hanebeck
Title : Informationsfusion für verteilte Systeme
In : Informationsfusion in der Mess- und Sensortechnik
Address :
Date : September 2006
Abstract
Dieser Beitrag befasst sich mit modellbasierten Methoden zur Vermessung
verteilter physikalischer Phänomene. Diese Methoden zeichnen
sich durch eine systematische Behandlung stochastischer Unsicherheiten
aus, so dass neben der Rekonstruktion der vollständigen Wahrscheinlichkeitsdichte
der relevanten Größen aus einer geringen Anzahl von zeit-,
orts- und wertdiskreten Messungen auch die Generierung optimaler
Messsequenzen möglich ist. Es wird dargestellt, wie eine Beschreibung
für ein verteilt-parametrisches System in Form einer partiellen
Differentialgleichung, welche einen unendlich-dimensionalen Zustandsraum
beschreibt, in eine konzentriert-parametrische Form konvertiert
wird. Diese kann als Grundlage für den Entwurf klassischer Schätzer,
wie z.B. des Kalman Filters, dienen. Ferner wird eine Methode zur
Sensoreinsatzplanung vorgestellt, mit der eine optimale Sequenz von
Messparametern bestimmt werden kann, um mit einem minimalen Messaufwand
die Unsicherheit auf ein gewünschtes Maß zu reduzieren. Die
Anwendung dieser Methoden wird an zwei Beispielen, einer Temperaturverteilung
und der Verformung einer Führungsschiene, demonstriert. Zusätzlich
werden die Herausforderungen bei der Behandlung nichtlinearer Systeme
und die Probleme bei der dezentralen Verarbeitung, wie sie typischerweise
beim Einsatz von Sensornetzwerken auftreten, diskutiert.
Frederik Beutler, Uwe D. Hanebeck,
The Probabilistic Instantaneous Matching Algorithm
Proceedings of the 2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2006), pp. 311-316, Heidelberg, Germany, September 2006.
PDF
Author : Frederik Beutler, Uwe D. Hanebeck
Title : The Probabilistic Instantaneous Matching Algorithm
In : Proceedings of the 2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2006)
Address : Heidelberg, Germany
Date : September 2006
Abstract
A new Bayesian filtering technique for estimating signal parameters
directly from discrete-time sequences is introduced. The so called
probabilistic instantaneous matching algorithm recursively updates
the probability density function of the parameters for every received
sample and, thus, provides a high update rate up to the sampling
rate with high accuracy. In order to do so, one of the signal sequences
is used as part of a time-variant nonlinear measurement equation.
Furthermore, the time-variant nature of the parameters is explicitly
considered via a system equation, which describes the evolution of
the parameters over time. An important feature of the probabilistic
instantaneous matching algorithm is that it provides a probability
density function over the parameter space instead of a single point
estimate. This probability density function can be used in further
processing steps, e.g. a range based localization algorithm in the
case of time-of-arrival estimation.
Dietrich Brunn, Felix Sawo, Uwe D. Hanebeck,
Efficient Nonlinear Bayesian Estimation based on Fourier Densities
Proceedings of the 2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2006), pp. 312-322, Heidelberg, Germany, September 2006.
PDF
Author : Dietrich Brunn, Felix Sawo, Uwe D. Hanebeck
Title : Efficient Nonlinear Bayesian Estimation based on Fourier Densities
In : Proceedings of the 2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2006)
Address : Heidelberg, Germany
Date : September 2006
Abstract
Efficiently implementing nonlinear Bayesian estimators is still not
a fully solved problem. For practical applications, a trade-off between
estimation quality and demand on computational resources has to be
found. In this paper, the use of nonnegative Fourier series, so-called
Fourier densities, for Bayesian estimation is proposed. By using
the absolute square of Fourier series for the density representation,
it is ensured that the density stays nonnegative. Nonetheless, approximation
of arbitrary probability density functions can be made by using the
Fourier integral formula. An efficient Bayesian estimator algorithm
with constant complexity for nonnegative Fourier series is derived
and demonstrated by means of an example.
Marc P. Deisenroth, Toshiyuki Ohtsuka, Florian Weissel, Dietrich Brunn, Uwe D. Hanebeck,
Finite-Horizon Optimal State-Feedback Control of Nonlinear Stochastic Systems Based on a Minimum Principle
Proceedings of the 2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2006), pp. 371-376, Heidelberg, Germany, September 2006.
PDF
Author : Marc P. Deisenroth, Toshiyuki Ohtsuka, Florian Weissel, Dietrich Brunn, Uwe D. Hanebeck
Title : Finite-Horizon Optimal State-Feedback Control of Nonlinear Stochastic Systems Based on a Minimum Principle
In : Proceedings of the 2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2006)
Address : Heidelberg, Germany
Date : September 2006
Abstract
In this paper, an approach to the finite-horizon
optimal state-feedback control problem of nonlinear, stochastic,
discrete-time systems is presented. Starting from the dynamic
equation, the value function will be approximated
by means of Taylor series expansion up to second-order
derivatives. Moreover, the problem will be reformulated, such
that a minimum principle can be applied to the stochastic
problem. Employing this minimum principle, the optimal control
problem can be rewritten as a two-point boundary-value
problem to be solved at each time step of a shrinking horizon.
To avoid numerical problems, the two-point boundary-value
problem will be solved by means of a continuation method.
Thus, the curse of dimensionality of dynamic programming
is avoided, and good candidates for the optimal state-feedback
controls are obtained. The proposed approach will be evaluated
by means of a scalar example system.
Marco Huber, Dietrich Brunn, Uwe D. Hanebeck,
Closed-Form Prediction of Nonlinear Dynamic Systems by Means of Gaussian Mixture Approximation of the Transition Density
Proceedings of the 2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2006), pp. 98-103, Heidelberg, Germany, September 2006.
PDF
Author : Marco Huber, Dietrich Brunn, Uwe D. Hanebeck
Title : Closed-Form Prediction of Nonlinear Dynamic Systems by Means of Gaussian Mixture Approximation of the Transition Density
In : Proceedings of the 2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2006)
Address : Heidelberg, Germany
Date : September 2006
Abstract
Recursive prediction of the state of a nonlinear stochastic dynamic
system cannot be efficiently performed in general, since the complexity
of the probability density function characterizing the system state
increases with every prediction step. Thus, representing the density
in an exact closed-form manner is too complex or even impossible.
So, an appropriate approximation of the density is required. Instead
of directly approximating the predicted density, we propose the approximation
of the transition density by means of Gaussian mixtures. We treat
the approximation task as an optimization problem that is solved
offline via progressive processing to bypass initialization problems
and to achieve high quality approximations. Once having calculated
the transition density approximation offline, prediction can be performed
efficiently resulting in a closed-form density representation with
constant complexity.
Oliver C. Schrempf, Dietrich Brunn, Uwe D. Hanebeck,
Dirac Mixture Density Approximation Based on Minimization of the Weighted Cramér-von Mises Distance
Proceedings of the 2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2006), pp. 512-517, Heidelberg, Germany, September 2006.
PDF
Author : Oliver C. Schrempf, Dietrich Brunn, Uwe D. Hanebeck
Title : Dirac Mixture Density Approximation Based on Minimization of the Weighted Cramér-von Mises Distance
In : Proceedings of the 2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2006)
Address : Heidelberg, Germany
Date : September 2006
Abstract
This paper proposes a systematic procedure for approximating arbitrary
probability density functions by means of Dirac mixtures. For that
purpose, a distance measure is required, which is in general not
well defined for Dirac mixture densities. Hence, a distance measure
comparing the corresponding cumulative distribution functions is
employed. Here, we focus on the weighted Cramer-von Mises distance,
a weighted integral quadratic distance measure, which is simple and
intuitive. Since a closed-form solution of the given optimization
problem is not possible in general, an efficient solution procedure
based on a homotopy continuation approach is proposed. Compared to
a standard particle approximation, the proposed procedure ensures
an optimal approximation with respect to a given distance measure.
Although useful in their own respect, the results also provide the
basis for a recursive nonlinear filtering mechanism as an alternative
to the popular particle filters.
Hui Wang, Henning Lenz, Andrei Szabo, Uwe D. Hanebeck, Joachim Bamberger,
Fusion of Barometric Sensors, WLAN Signals and Building Information for 3-D Indoor/Campus Localization
Proceedings of the 2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2006), pp. 426-432, Heidelberg, Germany, September 2006.
PDF
Author : Hui Wang, Henning Lenz, Andrei Szabo, Uwe D. Hanebeck, Joachim Bamberger
Title : Fusion of Barometric Sensors, WLAN Signals and Building Information for 3-D Indoor/Campus Localization
In : Proceedings of the 2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2006)
Address : Heidelberg, Germany
Date : September 2006
Abstract
Location estimation in indoor/campus
environments has attracted much interest for its broad
applications. Many applications (e.g. personnel security)
require not only the 2-D coordinate but also the floor index
where the mobile users are situated. However, most of the
current location systems cannot provide the floor information
accurately and robustly. In this paper, we propose a 3-D
localization scheme which fuses the barometric sensor with
Wireless LAN (WLAN) signals and building information. Our
experiments show that this fusion scheme can both identify the
floor index without errors and improve the horizontal
localization accuracy. Moreover, since the barometric sensor is
quite simple and cheap, it would bring almost no increase in
system costs.
Patrick Rößler, Timothy Armstrong, Oliver Hessel, Michael Mende, Uwe D. Hanebeck,
A Novel Haptic Interface for Free Locomotion in Extended Range Telepresence Scenarios
Proceedings of the 3rd International Conference on Informatics in Control, Automation and Robotics (ICINCO 2006), pp. 148-153, Setúbal, Portugal, August 2006.
PDF
Author : Patrick Rößler, Timothy Armstrong, Oliver Hessel, Michael Mende, Uwe D. Hanebeck
Title : A Novel Haptic Interface for Free Locomotion in Extended Range Telepresence Scenarios
In : Proceedings of the 3rd International Conference on Informatics in Control, Automation and Robotics (ICINCO 2006)
Address : Setúbal, Portugal
Date : August 2006
Abstract
Telepresence gives a user the impression of actually
being present in a distant environment. A mobile teleoperator acts
as a proxy in this target environment, replicates the user's motion,
and records sensory information, which is transferred to the user
and displayed in real-time. As a result the user is immersed in the
target environment. The user can then control the teleoperator by
walking naturally. Motion Compression, a nonlinear mapping between
the user's and the robot's motion, allows exploration of large
target environments even from small user environments.
For manipulation tasks haptic feedback is important. However, current
haptic displays do not allow wide-area motion. In this work we present
our design of a novel haptic display for simultaneous wide area motion
and haptic interaction.
Felix Sawo, Kathrin Roberts, Uwe D. Hanebeck,
Bayesian Estimation of Distributed Phenomena using Discretized Representations of Partial Differential Equations
Proceedings of the 3rd International Conference on Informatics in Control, Automation and Robotics (ICINCO 2006), pp. 16-23, Setúbal, Portugal, August 2006.
PDF
Author : Felix Sawo, Kathrin Roberts, Uwe D. Hanebeck
Title : Bayesian Estimation of Distributed Phenomena using Discretized Representations of Partial Differential Equations
In : Proceedings of the 3rd International Conference on Informatics in Control, Automation and Robotics (ICINCO 2006)
Address : Setúbal, Portugal
Date : August 2006
Abstract
This paper addresses a systematic method for the reconstruction and
the prediction of a distributed phenomenon characterized by partial
differential equations, which is monitored by a sensor network. In
the first step, the infinite-dimensional partial differential equation,
i.e. distributed-parameter system, is spatially and temporally decomposed
leading to a finite-dimensional state space form. In the next step,
the state of the resulting lumped-parameter system, which provides
an approximation of the solution of the underlying partial differential
equations, is dynamically estimated under consideration of uncertainties
both occurring in the system and arising from noisy measurements.
By using the estimation results, several additional tasks can be
achieved by the sensor network, e.g. optimal sensor placement, optimal
scheduling, and model improvement. The performance of the proposed
model-based reconstruction method is demonstrated by means of simulations.
Vesa Klumpp, Dietrich Brunn, Uwe D. Hanebeck,
Approximate Nonlinear Bayesian Estimation Based on Lower and Upper Densities
Proceedings of the 9th International Conference on Information Fusion (Fusion 2006), Florence, Italy, July 2006.
PDF
Author : Vesa Klumpp, Dietrich Brunn, Uwe D. Hanebeck
Title : Approximate Nonlinear Bayesian Estimation Based on Lower and Upper Densities
In : Proceedings of the 9th International Conference on Information Fusion (Fusion 2006)
Address : Florence, Italy
Date : July 2006
Abstract
Recursive calculation of the probability density function characterizing
the state estimate of a nonlinear stochastic dynamic system in general
cannot be performed exactly, since the type of the density changes
with every processing step and the complexity increases. Hence, an
approximation of the true density is required. Instead of using a
single complicated approximating density, this paper is concerned
with bounding the true density from below and from above by means
of two simple densities. This provides a kind of guaranteed estimator
with respect to the underlying true density, which requires a mechanism
for ordering densities. Here, a partial ordering with respect to
the cumulative distributions is employed. Based on this partial ordering,
a modified Bayesian filter step is proposed, which recursively propagates
lower and upper density bounds. A specific implementation for piecewise
linear densities with finite support is used for demonstrating the
performance of the new approach in simulations.
Felix Sawo, Dietrich Brunn, Uwe D. Hanebeck,
Parameterized Joint Densities with Gaussian and Gaussian Mixture Marginals
Proceedings of the 9th International Conference on Information Fusion (Fusion 2006), Florence, Italy, July 2006.
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Author : Felix Sawo, Dietrich Brunn, Uwe D. Hanebeck
Title : Parameterized Joint Densities with Gaussian and Gaussian Mixture Marginals
In : Proceedings of the 9th International Conference on Information Fusion (Fusion 2006)
Address : Florence, Italy
Date : July 2006
Abstract
In this paper we attempt to lay the foundation for a novel filtering
technique for the fusion of two random vectors with imprecisely known
stochastic dependency. This problem mainly occurs in decentralized
estimation, e.g., of a distributed phenomenon, where the stochastic
dependencies between the individual states are not stored. Thus,
we derive parameterized joint densities with both Gaussian marginals
and Gaussian mixture marginals. These parameterized joint densities
contain all information about the stochastic dependencies between
their marginal densities in terms of a parameter vector xi, which
can be regarded as a generalized correlation parameter. Unlike the
classical correlation coeffcient, this parameter is a suffcient measure
for the stochastic dependency even characterized by more complex
density functions such as Gaussian mixtures. Once this structure
and the bounds of these parameters are known, bounding densities
containing all possible density functions could be found.
Oliver C. Schrempf, Anne Hanselmann, Uwe D. Hanebeck,
Efficient Representation and Fusion of Hybrid Joint Densities for Clusters in Nonlinear Hybrid Bayesian Networks
Proceedings of the 9th International Conference on Information Fusion (Fusion 2006), Florence, Italy, July 2006.
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Author : Oliver C. Schrempf, Anne Hanselmann, Uwe D. Hanebeck
Title : Efficient Representation and Fusion of Hybrid Joint Densities for Clusters in Nonlinear Hybrid Bayesian Networks
In : Proceedings of the 9th International Conference on Information Fusion (Fusion 2006)
Address : Florence, Italy
Date : July 2006
Abstract
Undirected cycles in Bayesian networks are often treated by using
clustering methods. This results in networks with nodes characterized
by joint probability densities instead of marginal densities. An
efficient representation of these hybrid joint densities is essential
especially in nonlinear hybrid networks containing continuous as
well as discrete variables. In this article we present a unified
representation of continuous, discrete, and hybrid joint densities.
This representation is based on Gaussian and Dirac mixtures and allows
for analytic evaluation of arbitrary hybrid networks without loosing
structural information, even for networks containing clusters. Furthermore
we derive update formulae for marginal and joint densities from a
system theoretic point of view by treating a Bayesian network as
a system of cascaded subsystems. Together with the presented mixture
representation of densities this yields an exact analytic updating
scheme.


Publikationen aus dem Jahr 2005

Andreas Rauh, Uwe D. Hanebeck,
Moment-Based Prediction Step for Nonlinear Discrete-Time Dynamic Systems Using Exponential Densities
Proceedings of the 44th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC 2005), Sevilla, Spain, December 2005.
PDF
Author : Andreas Rauh, Uwe D. Hanebeck
Title : Moment-Based Prediction Step for Nonlinear Discrete-Time Dynamic Systems Using Exponential Densities
In : Proceedings of the 44th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC 2005)
Address : Sevilla, Spain
Date : December 2005
Abstract
In this paper, an efficient approach for a momentbased
Bayesian prediction step for both linear and nonlinear
discrete-time dynamic systems using exponential densities with
polynomial exponents is proposed. The exact solution of the
prediction step is approximated by an exponential density
which minimizes the Kullback-Leibler distance. Compared to
other approaches, the user of this procedure can specify the
approximation quality by controlling the deviation between
the moments of the exact and the approximated solution.
Furthermore, this algorithm can also be used for the adaptation
of the order of the exponential densities either to improve the
approximation quality or to reduce the computational effort.
Henning Groenda, Fabian Nowak, Patrick Rößler, Uwe D. Hanebeck,
Telepresence Techniques for Controlling Avatar Motion in First Person Games
Intelligent Technologies for Interactive Entertainment (INTETAIN 2005), Madonna di Campiglio, Italy, November 2005.
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Author : Henning Groenda, Fabian Nowak, Patrick Rößler, Uwe D. Hanebeck
Title : Telepresence Techniques for Controlling Avatar Motion in First Person Games
In : Intelligent Technologies for Interactive Entertainment (INTETAIN 2005)
Address : Madonna di Campiglio, Italy
Date : November 2005
Abstract
First person games are computer games, in which the user
experiences the virtual game world from an avatar's view. This avatar is
the user's alter ego in the game. In this paper, we present a telepresence
interface for the first person game Quake III Arena, which gives the user
the impression of presence in the game and thus leads to identification
with his avatar. This is achieved by tracking the user's motion and using
this motion data as control input for the avatar. As the user is wearing a
head-mounted display and he perceives his actions affecting the virtual
environment, he fully immerses into the target environment. Without
further processing of the user's motion data, the virtual environment
would be limited to the size of the user's real environment, which is not
desirable. The use of Motion Compression, however, allows exploring an
arbitrarily large virtual environment while the user is actually moving in
an environment of limited size.
Kathrin Roberts, Gabór Szabó, Uwe D. Hanebeck,
Sensorgestützte Bewegungssynchronisation von Operationsinstrumenten am schlagenden Herzen
Autonome Mobile Systeme 2005 (AMS 2005), 19. Fachgespräch, Stuttgart, Informatik Aktuell, pp. 269-275, Springer, October 2005.
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Author : Kathrin Roberts, Gabór Szabó, Uwe D. Hanebeck
Title : Sensorgestützte Bewegungssynchronisation von Operationsinstrumenten am schlagenden Herzen
In : Autonome Mobile Systeme 2005 (AMS 2005), 19. Fachgespräch, Stuttgart, Informatik Aktuell
Address :
Date : October 2005
Abstract
Offene oder minimal invasive Operationen am schlagenden Herzen erfordern
von dem Chirurgen eine hohe Konzentrationsfähigkeit über
einen längeren Zeitraum. Daher ist es für den Chirurgen sehr
hilfreich durch ein robotergestütztes Chirurgiesystem unterstützt
zu werden, das die Instrumente im Interventionsareal mit der Herzbewegung
synchronisiert. Um eine Bewegungskompensation durchzuführen,
muss ein Mechanismus gefunden werden, der aufgrund einer Prädiktion
der Herzbewegung die Instrumente nachführt. Für die Prädiktion
der Herzbewegung wird in diesem Artikel ein Verfahren zum Entwurf
eines stochastischen 3D-Bewegungsmodells für die Herzoberfläche
gezeigt. Ein Schätzer nimmt dieses Modell als Grundlage und verwendet
die verrauschten Sensormessungen von Landmarken der Herzoberfläche
um die Herzoberflächenbewegung zu prädizieren.
Kai Briechle, Uwe D. Hanebeck,
Lokalisierung mittels mengenbasierter nichtlinearer Filterung im Hyperr