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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)
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.

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)
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.

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)
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.

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)
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

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)
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 2012

Proceedings of the 15th International Conference on Information Fusion (Fusion 2012) (to appear), Singapore, July, 2012.

Author : Marcus Baum, Florian Faion, Uwe D. Hanebeck
Title : Modeling Extended Targets as Multiplicative Noise (preliminary title)
In : Proceedings of the 15th International Conference on Information Fusion (Fusion 2012) (to appear)
Date : July 2012

Proceedings of the 15th International Conference on Information Fusion (Fusion 2012) (to appear), Singapore, July, 2012.

Author : Marcus Baum, Peter Willett, Uwe D. Hanebeck
Title : Calculating Exact MMOSPA estimates for Particle Densities (preliminary title)
In : Proceedings of the 15th International Conference on Information Fusion (Fusion 2012) (to appear)
Date : July 2012

Proceedings of the 15th International Conference on Information Fusion (Fusion 2012) (to appear), Singapore, July, 2012.

Author : Florian Faion, Marcus Baum, Uwe D. Hanebeck
Title : Tracking 3D Shapes in Noisy Point Clouds with Random Hypersurface Models (preliminary title)
In : Proceedings of the 15th International Conference on Information Fusion (Fusion 2012) (to appear)
Date : July 2012

Proceedings of the 15th International Conference on Information Fusion (Fusion 2012) (to appear), Singapore, July, 2012.

Author : Florian Faion, Patrick Ruoff, Antonio Zea, Uwe D. Hanebeck
Title : Recursive Bayesian Calibration of RGBD-Cameras with Non-Overlapping Views (preliminary title)
In : Proceedings of the 15th International Conference on Information Fusion (Fusion 2012) (to appear)
Date : July 2012

Proceedings of the 15th International Conference on Information Fusion (Fusion 2012) (to appear), Singapore, July, 2012.

Author : Jörg Fischer, Achim Hekler, Uwe D. Hanebeck
Title : State Estimation in Packet-Based Networked Control Systems (preliminary title)
In : Proceedings of the 15th International Conference on Information Fusion (Fusion 2012) (to appear)
Date : July 2012

Proceedings of the 15th International Conference on Information Fusion (Fusion 2012) (to appear), Singapore, July, 2012.

Author : Igor Gilitschenski, Uwe D. Hanebeck
Title : A Robust Computational Test for Overlapping of two Arbitrary-dimensional Ellipsoids in Fault-Detection of Kalman Filters (preliminary title)
In : Proceedings of the 15th International Conference on Information Fusion (Fusion 2012) (to appear)
Date : July 2012

Proceedings of the 15th International Conference on Information Fusion (Fusion 2012) (to appear), Singapore, July, 2012.

Author : Uwe D. Hanebeck, Jannik Steinbring
Title : Progressive Gaussian Filtering (preliminary title)
In : Proceedings of the 15th International Conference on Information Fusion (Fusion 2012) (to appear)
Date : July 2012

Proceedings of the 15th International Conference on Information Fusion (Fusion 2012) (to appear), Singapore, July, 2012.

Author : Achim Hekler, Jörg Fischer, Uwe D. Hanebeck
Title : Optimal Stochastic Open-Loop Feedback Control over Unreliable Networks (preliminary title)
In : Proceedings of the 15th International Conference on Information Fusion (Fusion 2012) (to appear)
Date : July 2012

Proceedings of the 15th International Conference on Information Fusion (Fusion 2012) (to appear), Singapore, July, 2012.

Author : Benjamin Noack, Florian Pfaff, Uwe D. Hanebeck
Title : Combined Stochastic and Set-membership Information Filtering in Multisensor Systems (preliminary title)
In : Proceedings of the 15th International Conference on Information Fusion (Fusion 2012) (to appear)
Date : July 2012

Proceedings of the 15th International Conference on Information Fusion (Fusion 2012) (to appear), Singapore, July, 2012.

Author : Ferdinand Packi, Uwe D. Hanebeck
Title : Robust NLOS Discrimination for Acoustic Pose Tracking using Real-time Signal Processing (preliminary title)
In : Proceedings of the 15th International Conference on Information Fusion (Fusion 2012) (to appear)
Date : July 2012

Proceedings of the 15th International Conference on Information Fusion (Fusion 2012) (to appear), Singapore, July, 2012.

Author : Marc Reinhardt, Benjamin Noack, Uwe D. Hanebeck
Title : An Extension to Exact T2TF for Consistent Distributed Data Fusion (preliminary title)
In : Proceedings of the 15th International Conference on Information Fusion (Fusion 2012) (to appear)
Date : July 2012

Proceedings of the 15th International Conference on Information Fusion (Fusion 2012) (to appear), Singapore, July, 2012.

Author : Marc Reinhardt, Benjamin Noack, Uwe D. Hanebeck
Title : Closed-form Optimization of Covariance Intersection for Low-Dimensional Matrices (preliminary title)
In : Proceedings of the 15th International Conference on Information Fusion (Fusion 2012) (to appear)
Date : July 2012

Proceedings of the 2012 American Control Conference (ACC 2012) (to appear), Montréal, Canada, June, 2012.

Author : Achim Hekler, Christof Chlebek, Uwe D. Hanebeck
Title : Efficient Open-Loop Feedback Control of Nonlinear Stochastic Systems Based on Deterministic Dirac Mixture Densities
In : Proceedings of the 2012 American Control Conference (ACC 2012) (to appear)
Date : June 2012

Proceedings of the 2012 American Control Conference (ACC 2012) (to appear), Montréal, Canada, June, 2012.

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) (to appear)
Date : June 2012

Signal Processing, Sensor Fusion, and Target Recognition XXI (Proceedings of SPIE), Baltimore, Maryland, USA, April, 2012.

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)
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.

IEEE Aerospace and Electronic Systems Magazine, accepted March 2012 (to appear).

Author : Marcus Baum, Uwe D. Hanebeck
Title : Simultaneous Tracking and Shape Estimation of Extended Targets
In : IEEE Aerospace and Electronic Systems Magazine
Date : accepted March 2012 (to appear)

Proceedings of the Transportation Research Board 91st Annual Meeting (TRB 2012), Washington D. C., USA, January, 2012.

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)
Date : January 2012

Publikationen aus dem Jahr 2011

IEEE Transactions on Aerospace and Electronic Systems, accepted August 2011 (to appear).

Author : Marcus Baum, Uwe D. Hanebeck
Title : A Novel Approach for Extended Object Tracking based on Combined Set-Theoretic and Stochastic Fusion
In : IEEE Transactions on Aerospace and Electronic Systems
Date : accepted August 2011 (to appear)

Proceedings of the 50th IEEE Conference on Decision and Control (CDC 2011), Orlando, Florida, USA, December, 2011.

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)
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.

Proceedings of the 50th IEEE Conference on Decision and Control (CDC 2011), Orlando, Florida, USA, December, 2011.

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)
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.

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.

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)
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.

Proceedings of the 2011 11th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2011), Bled, Slovenia, October, 2011.

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)
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.

Proceedings of the IEEE ISIF Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF 2011), Berlin, Germany, October, 2011.

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)
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.

Proceedings of the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011), San Francisco, California, USA, September, 2011.

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)
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.

Proceedings of the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011), San Francisco, California, USA, September, 2011.

Author : Dirk Gehrig, Peter Krauthausen, Lukas Rybok, Hildegard Kühne, Tanja Schultz, Uwe D. Hanebeck, Rainer Stiefelhagen
Title : Combined Multi-Level Intention, Activity, and Motion Recognition for a Humanoid Robot
In : Proceedings of the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011)
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.

Proceedings of the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011), San Francisco, California, USA, September, 2011.

Author : Antonia Pérez Arias, Uwe D. Hanebeck
Title : Motion Control of a Semi-Mobile Haptic Interface for Haptic Interaction in Arbitrarily-Sized Target Environments
In : Proceedings of the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011)
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.

Proceedings of the Traffic and Granular Flow Conference 2011 (TGF 2011), Moscow, Russia, September, 2011.

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)
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.

Proceedings of the 18th IFAC World Congress (IFAC 2011), Milan, Italy, August, 2011.

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)
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.

Proceedings of the 18th IFAC World Congress (IFAC 2011), Milan, Italy, August, 2011.

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)
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.

Proceedings of the 14th International Conference on Information Fusion (Fusion 2011), Chicago, Illinois, USA, July, 2011.

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)
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

Proceedings of the 14th International Conference on Information Fusion (Fusion 2011), Chicago, Illinois, USA, July, 2011.

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)
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.

Proceedings of the 14th International Conference on Information Fusion (Fusion 2011), Chicago, Illinois, USA, July, 2011.

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)
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.

Proceedings of the 14th International Conference on Information Fusion (Fusion 2011), Chicago, Illinois, USA, July, 2011.

Author : Evgeniya Bogatyrenko, Uwe D. Hanebeck
Title : Adaptive Model-Based Visual Stabilization of Image Sequences
In : Proceedings of the 14th International Conference on Information Fusion (Fusion 2011)
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.

Proceedings of the 14th International Conference on Information Fusion (Fusion 2011), Chicago, Illinois, USA, July, 2011.

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)
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.

Proceedings of the 14th International Conference on Information Fusion (Fusion 2011), Chicago, Illinois, USA, July, 2011.

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)
Date : July 2011

Abstract

Many modern fusion architectures are designed to process and fuse data in networked systems. %local nodes can operate independently
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.

Proceedings of the 14th International Conference on Information Fusion (Fusion 2011), Chicago, Illinois, USA, July, 2011.

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)
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.

Proceedings of the 14th International Conference on Information Fusion (Fusion 2011), Chicago, Illinois, USA, July, 2011.

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)
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.

Proceedings of the 2011 American Control Conference (ACC 2011), San Francisco, California, USA, June, 2011.

Author : Evgeniya Bogatyrenko, Uwe D. Hanebeck
Title : Visual Stabilization of a Beating Heart Motion by Model-Based Transformation of Image Sequences
In : Proceedings of the 2011 American Control Conference (ACC 2011)
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.

Proceedings of the 2011 American Control Conference (ACC 2011), San Francisco, California, USA, June, 2011.

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)
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.

Proceedings of the 2011 American Control Conference (ACC 2011), San Francisco, California, USA, June, 2011.

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)
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.

Proceedings of the 2011 American Control Conference (ACC 2011), San Francisco, California, USA, June, 2011.

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)
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.

Proceedings of the IEEE World Haptics Conference (WHC 2011), Istanbul, Turkey, June, 2011.

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)
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.

Autonomous Robots and Multirobot Systems (ARMS) Workshop at AAMAS 2011, Taipei, Taiwan, May, 2011.

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
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.

Publikationen aus dem Jahr 2010

Proceedings of the 2010 IEEE Conference on Decision and Control (CDC 2010), Atlanta, Georgia, USA, December, 2010.

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)
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.

tm - Technisches Messen, Oldenbourg Verlag, 77(10):544-550, October, 2010.

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
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.

tm - Technisches Messen, Oldenbourg Verlag, 77(10):551-557, October, 2010.

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
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.

Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010), Taipei, Taiwan, October, 2010.

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)
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.

Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010), Taipei, Taiwan, October, 2010.

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)
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.

Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010), Taipei, Taiwan, October, 2010.

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)
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.

Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010), Taipei, Taiwan, October, 2010.

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)
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.

Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010), Taipei, Taiwan, October, 2010.

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)
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.

Proceedings of the IEEE ISIF Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF 2010), Leipzig, Germany, October, 2010.

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)
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.

International Journal of Computer Assisted Radiology and Surgery (IJCARS 2010), 6(3):387-399, August, 2010.

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)
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.

33rd Annual German Conference on Artificial Intelligence (KI 2010), Karlsruhe, Germany, September, 2010.

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)
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.

Proceedings of the 2010 IEEE International Conference on Indoor Positioning and Indoor Navigation (IPIN 2010), Zürich, Switzerland, September, 2010.

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)
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.

Proceedings of the 2010 IEEE International Conference on Virtual Environments, Human-Computer Interfaces and Measurement Systems (VECIMS 2010), Taranto, Italy, September, 2010.

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)
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.

Proceedings of the 2010 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2010), Salt Lake City, Utah, USA, September, 2010.

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)
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.

Proceedings of the 2010 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2010), Salt Lake City, Utah, USA, September, 2010.

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)
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.

Proceedings of the 2010 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2010), Salt Lake City, Utah, USA, September, 2010.

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)
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.

Proceedings of the 2010 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2010), Salt Lake City, Utah, USA, September, 2010.

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)
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.

Proceedings of the 2010 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2010), Salt Lake City, Utah, USA, September, 2010.

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)
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.

Proceedings of the 2010 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2010), Salt Lake City, Utah, USA, September, 2010.

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)
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 Cramér-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.

Proceedings of the 2010 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2010), Salt Lake City, Utah, USA, September, 2010.

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)
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 Cramér-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.

Proceedings of the 2010 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2010), Salt Lake City, Utah, USA, September, 2010.

Author : Daniel Lyons, Achim Hekler, Markus Kuderer, Uwe D. Hanebeck
Title : Robust Model Predictive Control Incorporating Least Favorable Measurements
In : Proceedings of the 2010 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2010)
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

Proceedings of the IEEE Multi-Conference on Systems and Control (MSC 2010), Yokohama, Japan, September, 2010.

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)
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.

Proceedings of the 13th International Conference on Information Fusion (Fusion 2010), Edinburgh, United Kingdom, July, 2010.

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)
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.

Proceedings of the 13th International Conference on Information Fusion (Fusion 2010), Edinburgh, United Kingdom, July, 2010.

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)
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.

Proceedings of the 13th International Conference on Information Fusion (Fusion 2010), Edinburgh, United Kingdom, July, 2010.

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)
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.

Proceedings of the 13th International Conference on Information Fusion (Fusion 2010), Edinburgh, United Kingdom, July, 2010.

Author : Patrick Dunau, Ferdinand Packi, Frederik Beutler, Uwe D. Hanebeck
Title : Efficient Multilateration Tracking System with Concurrent Offset Estimation using Stochastic Filtering Techniques
In : Proceedings of the 13th International Conference on Information Fusion (Fusion 2010)
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.

Proceedings of the 13th International Conference on Information Fusion (Fusion 2010), Edinburgh, United Kingdom, July, 2010.

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)
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.

Proceedings of the 13th International Conference on Information Fusion (Fusion 2010), Edinburgh, United Kingdom, July, 2010.

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)
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.

Proceedings of the 13th International Conference on Information Fusion (Fusion 2010), Edinburgh, United Kingdom, July, 2010.

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)
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.

Proceedings of the 13th International Conference on Information Fusion (Fusion 2010), Edinburgh, United Kingdom, July, 2010.

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)
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 modified 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.

Proceedings of the 13th International Conference on Information Fusion (Fusion 2010), Edinburgh, United Kingdom, July, 2010.

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)
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.

Proceedings of the IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing (SUTC 2010), Newport Beach, California, USA, June, 2010.

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)
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.

Proceedings of the 2010 American Control Conference (ACC 2010), Baltimore, Maryland, USA, June, 2010.

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)
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 Cramér-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.

Proceedings of the 2010 American Control Conference (ACC 2010), Baltimore, Maryland, USA, June, 2010.

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)
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.

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
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.

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
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.

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
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.

Proccedings of the 5th International Conference on Pedestrian and Evacuation Dynamics (PED 2010), Springer-Verlag, Gaithersburg, Maryland, USA, March, 2010.

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
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

Proceedings of the 9th IEEE International Symposium on Signal Processing and Information Technology (ISSPIT 2009), Ajman, United Arab Emirates, December, 2009.

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)
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.

Proceedings of the 2009 IEEE Conference on Decision and Control (CDC 2009), Shanghai, China, December, 2009.

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)
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 Cramér–
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.

Proceedings of the 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2009), October, 2009.

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)
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.

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.

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)
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.

Proceedings of the 2009 IEEE International Conference on Control Applications (CCA 2009), July, 2009.

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)
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.

Proceedings of the 12th International Conference on Information Fusion (Fusion 2009), Seattle, Washington, USA, July, 2009.

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)
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.

Proceedings of the 12th International Conference on Information Fusion (Fusion 2009), Seattle, Washington, USA, July, 2009.

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)
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.

Proceedings of the 12th International Conference on Information Fusion (Fusion 2009), Seattle, Washington, USA, July, 2009.

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)
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.

Proceedings of the 12th International Conference on Information Fusion (Fusion 2009), Seattle, Washington, USA, July, 2009.

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)
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.

Proceedings of the 12th International Conference on Information Fusion (Fusion 2009), Seattle, Washington, USA, July, 2009.

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)
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.

Proceedings of the 12th International Conference on Information Fusion (Fusion 2009), Seattle, Washington, USA, July, 2009.

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)
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.

Proceedings of the 12th International Conference on Information Fusion (Fusion 2009), Seattle, Washington, USA, July, 2009.

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)
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.

Proceedings of the 12th International Conference on Information Fusion (Fusion 2009), Seattle, Washington, USA, July, 2009.

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)
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.

Proceedings of the 6th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2009), pp. 222-227, Milan, Italy, July, 2009.

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)
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.

26th International Conference on Machine Learning (ICML 2009) in Montreal, Canada, June, 2009.

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
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).

Proceedings of the 22nd Annual Conference on Computer Animation and Social Agents (CASA 2009), pp. 13-16, Amsterdam, The Netherlands, June, 2009.

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)
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.

IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2009) in Taipei, Taiwan, pp. 3413-3416, April, 2009.

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
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.

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)
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

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
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.

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
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.

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.

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)
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.

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.

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)
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.

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.

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)
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.

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.

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)
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.

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.

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)
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.

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.

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)
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

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.

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)
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.

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.

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)
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.

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.

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)
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.

Proceedings of the 17th IFAC World Congress (IFAC 2008), 17, Seoul, Republic of Korea, July, 2008.

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)
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.

Proceedings of the 17th IFAC World Congress (IFAC 2008), 17, Seoul, Republic of Korea, July, 2008.

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)
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.

Proceedings of the 17th IFAC World Congress (IFAC 2008), 17, Seoul, Republic of Korea, July, 2008.

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)
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).

Proceedings of the 11th International Conference on Information Fusion (Fusion 2008), pp. 1-8, Cologne, Germany, July, 2008.

Author : Marco F. Huber, Uwe D. Hanebeck
Title : Progressive Gaussian Mixture Reduction
In : Proceedings of the 11th International Conference on Information Fusion (Fusion 2008)
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.

Proceedings of the 11th International Conference on Information Fusion (Fusion 2008), pp. 1-8, Cologne, Germany, July, 2008.

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)
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.

Proceedings of the 11th International Conference on Information Fusion (Fusion 2008), pp. 1-8, Cologne, Germany, July, 2008.

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)
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.

Proceedings of the 11th International Conference on Information Fusion (Fusion 2008), pp. 1-8, Cologne, Germany, July, 2008.

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)
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.

Proceedings of the 11th International Conference on Information Fusion (Fusion 2008), pp. 1-8, Cologne, Germany, July, 2008.

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)
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.

Proceedings of the 11th International Conference on Information Fusion (Fusion 2008), pp. 1-7, Cologne, Germany, July, 2008.

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)
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.

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
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

Proceedings of the 2007 IEEE Conference on Decision and Control (CDC 2007), pp. 3065-3071, New Orleans, Louisiana, USA, December, 2007.

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)
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.

Proceedings of the 2007 IEEE Conference on Decision and Control (CDC 2007), pp. 3661-3666, New Orleans, Louisiana, USA, December, 2007.

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)
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.

Proceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2007), pp. 1429-1434, San Diego, California, USA, November, 2007.

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)
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.

Proceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2007), pp. 2474-2479, San Diego, California, USA, November, 2007.

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)
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.

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.

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)
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.

Proceedings of the 2007 American Control Conference (ACC 2007), pp. 4425-4430, New York, New York, USA, July, 2007.

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)
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.

Proceedings of the 2007 American Control Conference (ACC 2007), pp. 1768-1774, New York, New York, USA, July, 2007.

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)
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.

Proceedings of the 2007 American Control Conference (ACC 2007), pp. 3751-3756, New York, New York, USA, July, 2007.

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)
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.

Proceedings of the 2007 European Control Conference (ECC 2007), Kos, Greece, July, 2007.

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)
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.

Proceedings of the 10th International Conference on Information Fusion (Fusion 2007), Quebec, Canada, July, 2007.

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)
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.

Proceedings of the 10th International Conference on Information Fusion (Fusion 2007), Quebec, Canada, July, 2007.

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)
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.

Proceedings of the 10th International Conference on Information Fusion (Fusion 2007), Quebec, Canada, July, 2007.

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)
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.

International Conference on Human-Computer Interaction (HCI 2007), Peking, China, July, 2007.

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)
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.

Proceedings of 21st International Congress and Exhibition on Computer Assisted Radiology and Surgery (CARS 2007), pp. 243-245, Berlin, Germany, June, 2007.

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)
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.

Proceedings of the 4th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2007), SPSMC:54-61, Angers, France, May, 2007.

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)
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.

Proceedings of the 4th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2007), SPSMC:62-69, Angers, France, May, 2007.

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)
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.

Proceedings of the 2007 IEEE International Conference on Robotics and Automation (ICRA 2007), pp. 2261-2266, Rome, Italy, April, 2007.

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)
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.

International Conference on Information Processing in Sensor Networks (IPSN 2007), pp. 283-292, Cambridge, Massachusetts, USA, April, 2007.

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)
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.

tm - Technisches Messen, Oldenbourg Verlag, 3:75-90, March, 2007.

Author : Dietrich Brunn, Felix Sawo, Uwe D. Hanebeck
Title : Modellbasierte Vermessung verteilter Phänomene und Generierung optimaler Messsequenzen
In : tm - Technisches Messen, Oldenbourg Verlag
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.

Workshop on Positioning, Navigation and Communication, (WPNC 2007), Hanover, Germany, March, 2007.

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)
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

Proceedings of the 2006 IEEE Conference on Decision and Control (CDC 2006), pp. 1303-1308, San Diego, California, USA, December, 2006.

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)
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.

Proceedings of the 2006 IEEE Conference on Decision and Control (CDC 2006), San Diego, California, USA, December, 2006.

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)
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.

Proceedings of the 2006 IEEE International Conference on Control Applications (CCA 2006), Munich, Germany, October, 2006.

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)
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.

2nd International Workshop on Human Centered Robotic Systems (HCRS 2006), pp. 85-89, Munich, Germany, October, 2006.

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)
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.

2nd International Workshop on Human Centered Robotic Systems (HCRS 2006), pp. 7-12, Munich, Germany, October, 2006.

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)
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.

Proceedings of the 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2006), pp. 5189-5194, Beijing, China, October, 2006.

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)
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.

Informationsfusion in der Mess- und Sensortechnik, pp. 75-90, Universitätsverlag Karlsruhe, September, 2006.

Author : Dietrich Brunn, Felix Sawo, Uwe D. Hanebeck
Title : Informationsfusion für verteilte Systeme
In : Informationsfusion in der Mess- und Sensortechnik
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.

Proceedings of the 2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2006), pp. 311-316, Heidelberg, Germany, September, 2006.

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)
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.

Proceedings of the 2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2006), pp. 312-322, Heidelberg, Germany, September, 2006.

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)
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.

Proceedings of the 2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2006), pp. 371-376, Heidelberg, Germany, September, 2006.

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)
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.

Proceedings of the 2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2006), pp. 98-103, Heidelberg, Germany, September, 2006.

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)
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.

Proceedings of the 2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2006), pp. 512-517, Heidelberg, Germany, September, 2006.

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)
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.

Proceedings of the 2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2006), pp. 426-432, Heidelberg, Germany, September, 2006.

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)
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.

Proceedings of the 3rd International Conference on Informatics in Control, Automation and Robotics (ICINCO 2006), pp. 148-153, Setúbal, Portugal, August, 2006.

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)
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.

Proceedings of the 3rd International Conference on Informatics in Control, Automation and Robotics (ICINCO 2006), pp. 16-23, Setúbal, Portugal, August, 2006.

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)
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.

Proceedings of the 9th International Conference on Information Fusion (Fusion 2006), Florence, Italy, July, 2006.

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)
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.

Proceedings of the 9th International Conference on Information Fusion (Fusion 2006), Florence, Italy, July, 2006.

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)
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.

Proceedings of the 9th International Conference on Information Fusion (Fusion 2006), Florence, Italy, July, 2006.

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)
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

Proceedings of the 44th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC 2005), Sevilla, Spain, December, 2005.

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)
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.

Intelligent Technologies for Interactive Entertainment (INTETAIN 2005), Madonna di Campiglio, Italy, November, 2005.

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)
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.

Autonome Mobile Systeme 2005 (AMS 2005), 19. Fachgespräch, Stuttgart, Informatik Aktuell, pp. 269-275, Springer, October, 2005.

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
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.

at - Automatisierungstechnik, 53:415-424, September, 2005.

URL

Author : Kai Briechle, Uwe D. Hanebeck
Title : Lokalisierung mittels mengenbasierter nichtlinearer Filterung im Hyperraum
In : at - Automatisierungstechnik
Date : September 2005

Abstract

In recent years methods for localization of objects
have found widespread use as the basis for numerous novel technical
applications. The majority of these methods are based on filtering
algorithms which are used to estimate the position and orientation of
the object that shall be localized. In this article, a novel efficient
nonlinear filtering concept is presented. It is well suited for localization
problems for which conventional filtering algorithms based on linearization
do not yield satisfactory results. The performance of the proposed
algorithm is demonstrated by applications to prototypical localization
problems and an experiment, in which a mobile robot is localized.

Proceedings of the 7th International Workshop on Computer Science and Information Technologies (CSIT 2005), 1, Ufa, Russian Federation, September, 2005.

Author : Florian Weissel, Uwe D. Hanebeck
Title : A Test-Environment for Control Schemes in the Field of Collaborative Robots and Swarm Intelligence
In : Proceedings of the 7th International Workshop on Computer Science and Information Technologies (CSIT 2005)
Date : September 2005

Abstract

This paper presents an architecture for a test-environment for algorithms
and control schems in the filed of collaborative robotics and swarm
intelligence. As the foundation of the test-environment, small bionically
inspired robots are presented. The robots are small (20 cm x 5 cm
x 5 cm) and lightweight (< 200g). Their design is inspired by the
movement of caterpillars. Threee cubical segments are connected via
special joints, where each of these joints has three independent
degrees of translatory freedom. Thus, the robots are able to handle
rough terrain with small obstacle. The robots are driven by innovative
piezoelectric motors that allow a gearless design without any rotary
parts. Each robot is equipped with on-board processing and radio
communication. The software of the robots is written using TinyOS,
an event-driven operating system for large-scale distributed sensor-actuator-networks.

Proceedings of the 2nd International Conference on Informatics in Control, Automation and Robotics (ICINCO 2005), 2:146-153, Barcelona, Spain, September, 2005.

Author : Daniel Hahn, Frederik Beutler, Uwe D. Hanebeck
Title : Visual Scene Augmentation for Enhanced Human Perception
In : Proceedings of the 2nd International Conference on Informatics in Control, Automation and Robotics (ICINCO 2005)
Date : September 2005

Abstract

In this paper we present an assistive system for hearing-impaired
people that consists of a wearable microphone array and an Augmented
Reality (AR) system. This system helps the user in communication
situations, where many speakers or sources of background noise are
present. In order to restore the "cocktail party" effect multiple
microphones are used to estimate the position of individual sound
sources. In order to allow the user to interact in complex situations
with many speakers, an algorithm for estimating the user\'s attention
is developed. This algorithm determines the sound sources, which
are in the user\'s focus of attention. It allows the system to discard
irrelevant information and enables the user to focus on certain aspects
of the surroundings. Based on the user\'s hearing impairment, the
perception of the speaker in the focus of attention can be enhanced,
e.g. by amplification or using a speech-to-text conversion. A prototype
has been built for evaluating this approach. Currently the prototype
is able to locate sound beacons in three-dimensional space, to perform
a simple focus estimation, and to present floating captions in the
Augmented Reality. The prototype uses an intentionally simple user
interface, in order to minimize distractions.

Proceedings of the 2nd International Conference on Informatics in Control, Automation and Robotics (ICINCO 2005), 3:150-155, Barcelona, Spain, September, 2005.

Author : Patrick Rößler, Frederik Beutler, Uwe D. Hanebeck
Title : A Framework for Telepresent Game-Play in Large Virtual Environments
In : Proceedings of the 2nd International Conference on Informatics in Control, Automation and Robotics (ICINCO 2005)
Date : September 2005

Abstract

In this paper we present a framework that provides a novel interface
to avatar control in immersive computer games. The user\'s motion
is tracked and transferred to to the game environment. This motion
data is used as control input for the avatar. The game graphics are
rendered according to the avatar\'s motion and presented to the user
on a head-mounted display. As a result, the user immerses into the
game environment and identifies with the avatar. However, without
further processing of the motion data, the virtual environment would
be limited to the size of the user\'s real environment, which is not
desirable. By using Motion Compression, the framework allows exploring
an arbitrarily large virtual environment while the user is actually
moving in an environment of limited size. Based on the proposed framework,
two game applications were implemented, a modification of a commercially
available game and a custom designed game. These two applications
prove, that a telepresence system using Motion Compression is a highly
intuitive interface to game control.

Proceedings of the 2nd International Conference on Informatics in Control, Automation and Robotics (ICINCO 2005), 3:251-256, Barcelona, Spain, September, 2005.

Author : Oliver C. Schrempf, Uwe D. Hanebeck
Title : A Generic Model for Estimating User Intentions in Human-Robot Cooperation
In : Proceedings of the 2nd International Conference on Informatics in Control, Automation and Robotics (ICINCO 2005)
Date : September 2005

Abstract

The recognition of user intentions is an important feature for humanoid
robots to make implicit and human-like interactions possible. In
this paper, we introduce a formal view on user-intentions in human-machine
interaction and how they can be estimated by observing user actions.
We use Hybrid Dynamic Bayesian Networks to develop a generic model
that includes connections between intentions, actions, and sensor
measurements. This model can be used to extend arbitrary human-machine
applications by intention recognition.

Proceedings of the 2005 IEEE International Conference on Intelligent Robots and Systems (IROS 2005), pp. 2495-2500, Edmonton, Canada, August, 2005.

Author : Patrick Rößler, Frederik Beutler, Uwe D. Hanebeck, Norbert Nitzsche
Title : Motion Compression Applied to Guidance of a Mobile Teleoperator
In : Proceedings of the 2005 IEEE International Conference on Intelligent Robots and Systems (IROS 2005)
Date : August 2005

Abstract

Telepresence aims at giving a human user the impression of being present
in a remote environment. However, the user is actually situated in
a user environment and his motion is tracked. A mobile teleoperator
in the remote environment replicates this motion. The user can thus
control the mobile teleoperator\'s locomotion by walking. A stereo-camera
system mounted on the mobile teleoperator constantly records live
camera images and transfers them to the user environment, where they
are presented to the user on a head-mounted display. This paper presents
a long distance experiment, in which a mobile teleoperator was controlled
over a standard internet connection by natural locomotion. Without
further processing of the user\'s motion data, however, only exploration
of a remote environment of the same size or smaller than the user
environment is possible. As this is not desirable, we use Motion
Compression, an optimal nonlinear transformation of the user\'s path.
This algorithm allows controlling free motion in an arbitrarily large
target environment from a limited user environment.

Proceedings of the 2005 IEEE International Workshop on Robot and Human Interactive Communication (ROMAN 2005), pp. 555-560, Nashville, Tennessee, USA, August, 2005.

Author : Oliver C. Schrempf, Uwe D. Hanebeck, Andreas J. Schmid., Heinz Wörn
Title : A Novel Approach to Proactive Human-Robot Cooperation
In : Proceedings of the 2005 IEEE International Workshop on Robot and Human Interactive Communication (ROMAN 2005)
Date : August 2005

Abstract

This paper introduces the concept of proactive execution of robot
tasks in the context of human-robot cooperation with uncertain knowledge
of the human\'s intentions. We present a system architecture that
defines the necessary modules of the robot and their interactions
with each other. The two key modules are the intention recognition
that determines the human user\'s intentions and the planner that
executes the appropriate tasks based on those intentions. We show
how planning conflicts due to the uncertainty of the intention information
are resolved by proactive execution of the corresponding task that
optimally reduces the system\'s uncertainly. Finally, we present an
algorithm for selecting this task and suggest a benchmark scenario.

at - Automatisierungstechnik, Sonderheft: Datenfusion in der Automatisierungstechnik, 53(7):314-321, July, 2005.

URL

Author : Marian Grigoras, Olga Feiermann, Uwe D. Hanebeck
Title : Data-Driven Modeling of Signal Strength Distributions for Localization in Cellular Radio Networks (Datengetriebene Modellierung von Feldstärkeverteilungen für die Ortung in zellulären Funknetzen)
In : at - Automatisierungstechnik, Sonderheft: Datenfusion in der Automatisierungstechnik
Date : July 2005

Abstract

In this article, we propose a novel approach to solving
the localization problem in cellular networks, with a focus on indoor environments.
The goal is to estimate a mobile user\'s position, based on measurements of signals
received from different base stations. Our solution uses the Progressive
Bayes estimation framework to model the distribution of signal measurements,
as obtained in a series of calibration measurements. In the localization step,
we compute the full probability density over the user position. We also show
that user motion models can be easily integrated in our solution.

Proceedings of the 8th International Conference on Information Fusion (Fusion 2005), Philadelphia, Pennsylvania, USA, July, 2005.

Author : Kathrin Roberts, Uwe D. Hanebeck
Title : Prediction and Reconstruction of Distributed Dynamic Phenomena Characterized by Linear Partial Differential Equations
In : Proceedings of the 8th International Conference on Information Fusion (Fusion 2005)
Date : July 2005

Abstract

A primary challenge for the reconstruction of continuous-time, continuous-amplitude
distributed parameter systems is the inclusion of recent discrete-time,
discreteamplitude, spatially discrete measurements. Hence, a systematic
method for data processing is required that also handles incomplete
and noisy data, e.g. data from a sensor network. This article presents
two approaches to the reconstruction of distributed parameter systems
that can be described by linear partial differential equations (PDEs)
and involve one or several discrete measurement points. In both approaches,
the linear PDE is first converted into a bank of linear lumped systems
by means of modal analysis. In addition, a measurement equation relating
state and (sensor) data is derived. In the second step, a Kalman
filter (KF) is used to dynamically estimate the state of the lumped
systems, which provides an approximation of the solution of the underlying
PDE. The first approach uses Fourier Analysis. The second approach
uses Fourier Analysis and the collocation method. The approaches
are both demonstrated for a simple linear inhomogeneous PDE, the
one-dimensional heat equation.

Proceedings of the 8th International Conference on Information Fusion (Fusion 2005), 1:85-92, Philadelphia, Pennsylvania, USA, July, 2005.

Author : Oliver C. Schrempf, O. Feiermann, Uwe D. Hanebeck
Title : Optimal Mixture Approximation of the Product of Mixtures
In : Proceedings of the 8th International Conference on Information Fusion (Fusion 2005)
Date : July 2005

Abstract

Gaussian mixture densities are very common today to describe arbitrary
structured uncertainties in various applications. Many of these applications
have to deal with the fusion of uncertainties, an operation that
is usually performed by multiplication of these densities. The product
of Gaussian mixtures can be calculated exactly, but the number of
mixture components in the resulting mixture increases in an exponential
way. Hence, it is essential to approximate this resulting mixture
with less components, to keep it tractable for further processing
steps. This paper introduces an approach to approximate the exact
product with a mixture that uses less components. The maximum approximation
error can be chosen by the user. This choice allows to trade accuracy
of the approximation for the number of mixture components used. This
is possible due to the usage of a progressive processing scheme that
calculates the product operation by means of a system of ordinary
differential equations. The solution of this system yields the parameters
of the desired Gaussian mixture.

Proceedings of the 16th IFAC World Congress (IFAC 2005), Prague, Czech Republic, July, 2005.

Author : Oliver C. Schrempf, Uwe D. Hanebeck
Title : Evaluation of Hybrid Bayesian Networks using Analytical Density Representations
In : Proceedings of the 16th IFAC World Congress (IFAC 2005)
Date : July 2005

Abstract

In this article, a new mechanism is described for modeling and evaluating
Hybrid Dynamic Bayesian networks. The approach uses Gaussian mixtures
and Dirac mixtures as messages to calculate marginal densities. As
these densities are approximated by means of Gaussian mixtures, any
desired precision is possible. The presented approach removes the
restrictions of sample based evaluation of Bayesian networks since
it uses an analytical approximation scheme for probability densities
which systematically minimizes the distance between the exact and
the approximate density.

Proceedings of the 2005 IEEE International Conference on Robotics and Automation (ICRA 2005), Barcelona, Spain, April, 2005.

Author : Dietrich Brunn, Uwe D. Hanebeck
Title : A Model-Based Framework for Optimal Measurements in Machine Tool Calibration
In : Proceedings of the 2005 IEEE International Conference on Robotics and Automation (ICRA 2005)
Date : April 2005

Abstract

Calibration is the procedure of quantifying mechanical depciencies
of machines and compensating them by appropriate adjustment. This
paper introduces a model- based measurement framework for improving
calibration procedures of machine tools. The goal is to precisely
estimate the mechanical depciencies based on a minimal number of
measurements. For that purpose, the mechanical depciencies of linear
and rotary joints are modeled using splines. Uncertainties of the
depciency model are formulated stochastically, which allows incorporation
of imprecise measurement data and prediction of optimal measurement
parameters. We derive a method for optimally estimating a set of
splines, i.e., joint errors, based on a set of measurements and for
predicting the optimal joint configuration for new measurements.

Proceedings of the 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2005), 4:989-992, Philadelphia, Pennsylvania, USA, March, 2005.

Author : Frederik Beutler, Uwe D. Hanebeck
Title : Closed-Form Range-Based Posture Estimation Based on Decoupling Translation and Orientation
In : Proceedings of the 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2005)
Date : March 2005

Abstract

For estimating the posture, i.e., position and orientation, of an
extended target based on range measurements, a new closed-form solution
is proposed, which is based on decoupling position and orientation.
For decoupling, any procedure for range-based localization of point
targets, i.e., for mere position estimation, can be used. The new
solution is suboptimal, but nevertheless provides good accuracy and
is very practical from an application point of view.

Publikationen aus dem Jahr 2004

Proceedings of the 6th International Workshop on Computer Science and Information Technologies (CSIT 2004), 1:32-37, Budapest, Hungary, October, 2004.

Author : Oliver C. Schrempf, Uwe D. Hanebeck
Title : A New Approach for Hybrid Bayesian Networks Using Full Densities
In : Proceedings of the 6th International Workshop on Computer Science and Information Technologies (CSIT 2004)
Date : October 2004

Abstract

In this article, a new mechanism is described for modeling and evaluating
hybrid Bayesian networks. The approach uses Gaussian mixtures and
Dirac mixtures as messages to calculate marginal densities. The mechanism
is proven to be exact, hence the accuracy of resulting marginals
is only dependending on the accuracy of the conditional densities.
As these densities are approximated by means of Gaussian mixtures,
any desired precision can be achieved. The presented approach removes
the restrictions concerning the ancestry of discrete nodes often
made in literature. Hence it enables the designer to model arbitrary
parent-child relationships using continuous and discrete variables.

Proceedings of the 3rd IEEE Conference on Sensors (Sensors 2004), 1:413-416, Vienna, Austria, October, 2004.

Author : Frederik Beutler, Uwe D. Hanebeck
Title : A New Nonlinear Filtering Technique for Source Localization
In : Proceedings of the 3rd IEEE Conference on Sensors (Sensors 2004)
Date : October 2004

Abstract

A new model-based approach for estimating the parameters of an arbitrary
transformation between two discrete-time sequences will be introduced.
One sequence is interpreted as part of a nonlinear measurement equation,
the other sequence is typically measured sequentially. Based on every
measured value, the probability density function of the parameters
is updated using a Bayesian approach. For the evolution of the system
over time, a system equation is included. The new approach provides
a high update rate for the desired parameters up to the sampling
rate with high accuracy. It will be demonstrated for source localization
of a speaker, where the parameters describe the position of the source.

Proceedings of the 2004 IEEE International Conference on Systems, Man and Cybernetics (SMC 2004), 1:53-58, The Hague, The Netherlands, October, 2004.

Author : Patrick Rößler, Uwe D. Hanebeck
Title : Telepresence Techniques for Exception Handling in Household Robots
In : Proceedings of the 2004 IEEE International Conference on Systems, Man and Cybernetics (SMC 2004)
Date : October 2004

Abstract

The hardware for humanoid household robots
will be available in the near future. As households are unstructured
domains and interaction with human users is ambiguous,
these robots encounter unexpected situations, so
called exceptions. These render the robot unusable, if not
handled fast and transparently to the user. Telepresent exception
handling gives the technical staff an intuitive interface
to the robots and is transparent to the client. We propose
a service center for telepresent exception handling, which
is directly available and thus allows the use of humanoid
domestic robots as soon as the corresponding hardware is
commercially available.

Proceedings of the 2004 IEEE International Conference on Mechatronics & Robotics (MechRob 2004), pp. 1447-1452, Aachen, Germany, September, 2004.

Author : Patrick Rößler, Uwe D. Hanebeck, Norbert Nitzsche
Title : Feedback Controlled Motion Compression for Extended Range Telepresence
In : Proceedings of the 2004 IEEE International Conference on Mechatronics & Robotics (MechRob 2004)
Date : September 2004

Abstract

Telepresence aims at giving a human user the
impression of being present in a remote environment. This
is achieved by having a robot, that is actually present in the
remote environment, gather visual data. This data is presented
to the user, who is wearing a head mounted display. In order
to extend telepresence to an intuitive user interface for robot
teleoperation, the user’s motion is tracked and replicated by
the robot. The user can now interact more naturally with
the remote environment simply by walking around. Without
further processing of the motion data, the size of the remote
environment is limited to the size of the user environment.
Using the motion compression algorithm allows users to be
telepresent in large target environments while the size of the
user environment is limited. However, as a consequence of
the influence of standard motion compression on the user’s
natural navigation, he tends to leave the desired path. This can
even lead to users leaving the user environment. We address
this problem by introducing controlled motion compression
which adds a feedback controller to motion compression.
This controller modifies the user’s perception of the target
environment in such a way, that he is controlled on the path.

Presence: Teleoperators & Virtual Environments, 13(1):44-60, February, 2004.

URL

Author : Norbert Nitzsche, Uwe D. Hanebeck, Günther Schmidt
Title : Motion Compression for Telepresent Walking in Large Target Environments
In : Presence: Teleoperators & Virtual Environments
Date : February 2004

Abstract

Telepresent walking allows visits to remote places such as museums,
exhibitions, architecture, or industrial sites with a high degree of realism.
While walking freely around in the user environment, the user sees the
remote environment through the eyes of a remote mobile teleoperator.
For that purpose, the user\'s motion is tracked and transferred to the
teleoperator. Without additional processing of the motion data, the size of
the remote environment to be explored is limited to the size of the user environment.
This paper proposes an extension of telepresent walking to arbitrarily large remote
or virtual spaces based on compressing wide-area motion into the available user space.
Motion compression is a novel approach and does not make use of scaling or
walking-in-place metaphors. Rather, motion compression introduces some deviation of
curvature between user motion and teleoperator motion. An optimization approach is
used to find the user path of minimum curvature deviation with respect to a given
predicted teleoperator path that fits inside the boundaries of the user environment.
Turning angles and travel distances are mapped with a 1:1 ratio to provide the desired
impression of realistic self-locomotion in the teleoperator\'s environment.
The effects of the curvature deviation on inconsistent perception of
locomotion are studied in two experiments.

IEEE Transactions on Robotics and Automation, 20:36-44, February, 2004.

URL

Author : Kai Briechle, Uwe D. Hanebeck
Title : Localization of a Mobile Robot Using Relative Bearing Measurements
In : IEEE Transactions on Robotics and Automation
Date : February 2004

Abstract

In this paper, the problem of recursive robot localization
based on relative bearing measurements is considered,
where unknown but bounded measurement uncertainties are
assumed. A common approach is to approximate the resulting
set of feasible states by simple-shaped bounding sets such as, e.g.,
axis-aligned boxes, and calculate the optimal parameters of this
approximation based on the measurements and prior knowledge.
In the novel approach presented here, a nonlinear transformation
of the measurement equation into a higher dimensional space is
performed. This yields a tight, possibly complex-shaped, bounding
set in a closed-form representation whose parameters can be
determined analytically for the measurement step. It is shown that
the new bound is superior to commonly used outer bounds.

Publikationen aus dem Jahr 2003

Proceedings of the 2003 IEEE Conference on Decision and Control (CDC 2003), pp. 5366-5371, Maui, Hawaii, USA, December, 2003.

Author : Uwe D. Hanebeck, Olga Feiermann
Title : Progressive Bayesian Estimation for Nonlinear Discrete-Time Systems: The Filter Step for Scalar Measurements and Multidimensional States
In : Proceedings of the 2003 IEEE Conference on Decision and Control (CDC 2003)
Date : December 2003

Abstract

This paper is concerned with recursively
estimating the internal state sequence of a discrete-time
dynamic system by processing a sequence of noisy measurements
taken from the system output. Recursive processing requires
some kind of sufficient statistic for representing the information
collected up to a certain time step. For this purpose,
the probability density functions of the state are
especially well suited. Once they are available,
almost any type of point estimate, e.g. mean, mode, or median,
can be derived. In the case of continuous states, however,
the exact probability density functions characterizing the state
estimate are in general either not feasible or not well suited
for recursive processing. Hence, approximations of the true densities
are generally inevitable, where Gaussian mixture approximations are
convenient for a number of reasons. However, calculating appropriate
mixture parameters that minimize a global measure of deviation from
the true density is a tough optimization task. Here, we propose
a new approximation method that minimizes the squared integral deviation
between the true density and its mixture approximation. Rather than
trying to solve the original problem, it is converted into a corresponding
system of explicit ordinary first-order differential equations. This system
of differential equations is then solved over a finite "time" interval,
which is an efficient way of calculating the desired optimal parameter values.
We focus on the measurement update in the important case of vector states and
scalar measurements. In addition, approximation densities with separable
kernels are assumed. It will be shown, that if the measurement nonlinearities
are also separable, the required multidimensional integrals can be reduced
to the product of one-dimensional integrals. For several important
types of measurement functions including polynomial measurement nonlinearities,
closed-form analytic expressions for the coefficients of the system of
differential equations are available.

Autonome Mobile Systeme 2003 (AMS 2003), 18. Fachgespräch, Karlsruhe, Informatik Aktuell, Springer, October, 2003.

Author : Patrick Rößler, Uwe D. Hanebeck, Marian Grigoras, Paul T. Pilgram, Joachim Bamberger, Clemens Hoffmann
Title : Automatische Kartographierung der Signalcharakteristik in Funknetzwerken
In : Autonome Mobile Systeme 2003 (AMS 2003), 18. Fachgespräch, Karlsruhe, Informatik Aktuell
Date : October 2003

Proceedings of the 2003 European Control Conference 2003 (ECC 2003), Cambridge, United Kingdom, September, 2003.

Author : Uwe D. Hanebeck, Joachim Horn
Title : Data Validation in the Presence of Imprecisely Known Correlations
In : Proceedings of the 2003 European Control Conference 2003 (ECC 2003)
Date : September 2003

Abstract

This paper derives fundamental results for data validation in
the presence of imprecisely known correlations. Given a constraint
on the maximum absolute correlation of a given estimate
and measurement data, a tight upper bound for the joint
covariance matrix is derived, which finally yields a modified
Mahalanobis distance. The special cases of one-dimensional
and two-dimensional random variables are discussed.

Proceedings of the 2003 European Control Conference (ECC 2003), Cambridge, United Kingdom, September, 2003.

Author : Joachim Horn, Uwe D. Hanebeck, Konrad Riegel, Kai Heesche, Werner Hauptmann
Title : Nonlinear Set-Theoretic Position Estimation of Cellular Phones
In : Proceedings of the 2003 European Control Conference (ECC 2003)
Date : September 2003

Abstract

Within the existing GSM standard, several measurements are
available that can be used for estimating the position of a cellular
phone. First, the timing advance (TA) gives an estimate
for the distance to the serving base station. Second, the signal
strengths (RXLEV) of neighbouring base stations can also be
interpreted as distance information. Both TA and RXLEV are
subject to measurement errors caused for example by shadowing,
reflections, and fast fading. Thus, a nonlinear set-theoretic
estimation technique based on pseudo ellipsoids is applied. The
uncertainty regions in the original space defined by the measurements
are transformed into a hyperspace of higher dimension
and described by pseudo ellipsoids. An approximation of
the set intersection of the pseudo ellipsoids can be calculated
recursively by a linear set-theoretic filter. The resulting pseudo
ellipsoid is transformed back into the original space, and the
position estimate is calculated as center of gravity of the resulting
uncertainty region. The algorithm is evaluated based on
the data of an extensive field trial in a rural area. Compared
to Cell ID, the accuracy is significantly increased by using TA
and RXLEV, reducing the mean error by half.

Proceedings of the 13th IFAC Symposium on System Identification (SYSID 2003), pp. 331-336, Rotterdam, Netherlands, August, 2003.

Author : Uwe D. Hanebeck
Title : Optimal Filtering of Nonlinear Systems Based on Pseudo Gaussian Densities
In : Proceedings of the 13th IFAC Symposium on System Identification (SYSID 2003)
Date : August 2003

Abstract

We consider the problem of estimating the state of a discrete–time
dynamic system comprising a linear system equation and a nonlinear measurement
equation based on measurements corrupted by non–Gaussian noise. The problem is
solved by recursively calculating the complete posterior density of the state given
the measurements. For representing the resulting non–Gaussian posterior, a new
exponential type density, the so called pseudo Gaussian density, is introduced. By
converting the original nonlinear system to an equivalent linear representation in
a higher–dimensional space, the parameters of the pseudo Gaussian posterior are
obtained by means of a linear estimator operating in the higher–dimensional space.
The resulting filtering algorithms are easy to implement and always guarantee valid
posterior densities.

Proceedings of the 2003 IEEE Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2003), pp. 173-178, Tokyo, Japan, July, 2003.

Author : Uwe D. Hanebeck
Title : Progressive Bayesian Estimation for Nonlinear Discrete-Time Systems: The Measurement Step
In : Proceedings of the 2003 IEEE Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2003)
Date : July 2003

Abstract

This paper is concerned with estimating the internal state
of a dynamic system by processing measurements taken from
the system output. An exact analytic representation of the
probability density functions characterizing the estimate may
not be possible to obtain. Even when available, it may be
too complex or not practical because, for example, recursive
application is required. Hence, approximations are generally
inevitable. Gaussian mixture approximations are convenient
for a number of reasons. However, calculating appropriate
mixture parameters that minimize a global measure of deviation
from the true density is a tough optimization task. Here,
we propose a new approximation method that minimizes the
squared integral deviation between the the true density and
its mixture approximation. Rather than trying to solve the
original problem, it is converted into a corresponding system
of explicit ordinary first–order differential equations. This
system of differential equations is then solved over a finite
“time” interval, which is an efficient way of calculating the
desired optimal parameter values. For polynomial measurement
nonlinearities, closed–form analytic expressions for the
coefficients of the system of differential equations are derived.

Proceedings of SPIE, AeroSense Symposium, 5099:256 - 267, Orlando, Florida, USA, May, 2003.

Author : Uwe D. Hanebeck, Kai Briechle, Andreas Rauh
Title : Progressive Bayes: A New Framework for Nonlinear State Estimation
In : Proceedings of SPIE, AeroSense Symposium
Date : May 2003

Abstract

This paper is concerned with recursively estimating the internal state of a nonlinear dynamic system by processing
noisy measurements and the known system input. In the case of continuous states, an exact analytic
representation of the probability density characterizing the estimate is generally too complex for recursive estimation
or even impossible to obtain. Hence, it is replaced by a convenient type of approximate density characterized
by a finite set of parameters. Of course, parameters are desired that systematically minimize a given measure of
deviation between the (often unknown) exact density and its approximation, which in general leads to a complicated
optimization problem. Here, a new framework for state estimation based on progressive processing is
proposed. Rather than trying to solve the original problem, it is exactly converted into a corresponding system
of explicit ordinary first–order differential equations. Solving this system over a finite “time” interval yields the
desired optimal density parameters.

Proceedings of SPIE, Vol. 5084, AeroSense Symposium, pp. 51-58, Orlando, Florida, USA, May, 2003.

Author : Joachim Horn, Uwe D. Hanebeck, Konrad Riegel, Kai Heesche, Werner Hauptmann
Title : Nonlinear Set-Theoretic Position Estimation of Cellular Phones
In : Proceedings of SPIE, Vol. 5084, AeroSense Symposium
Date : May 2003

Proceedings of SPIE, Vol. 5079, AeroSense Symposium, pp. 265-276, Orlando, Florida, USA, May, 2003.

Author : Norbert Nitzsche, Uwe D. Hanebeck, Günther Schmidt
Title : Motion Compression for Telepresent Walking in Large-Scale Remote Environments
In : Proceedings of SPIE, Vol. 5079, AeroSense Symposium
Date : May 2003

Abstract

Telepresent walking creates the sensation of walking through a target environment, which is not directly accessible
to a human, e.g. because it is remote, hazardous, or of inappropriate scale. A mobile teleoperator replicates user
motion and collects visual and auditory information from the target environment, which is then sent and displayed
to the user. While walking freely about the user environment, the user perceives the target environment with the
sensors of the teleoperator and feels as if walking through the target environment. Without additional processing
of the user’s motion data, the size of the target environment to be explored is limited to the size of the user
environment. Motion compression extends telepresent walking to arbitrarily large target environments without
making use of scaling or walking-in-place metaphors. Both travel distances and turning angles are mapped with
ratio 1:1.

Proceedings of SPIE, Vol. 5084, AeroSense Symposium, pp. 39-50, Orlando, Florida, USA, May, 2003.

Author : Andreas Rauh, Kai Briechle, Uwe D. Hanebeck, Clemens Hoffmann, Joachim Bamberger, Marian Grigoras
Title : Localization of DECT Mobile Phones Based on a New Nonlinear Filtering Technique
In : Proceedings of SPIE, Vol. 5084, AeroSense Symposium
Date : May 2003

Abstract

In this paper, nonlinear Bayesian filtering techniques are applied to the localization of mobile radio communication
devices. The application of this approach is demonstrated for the localization of DECT mobile telephones in
a scenario with several base stations and a mobile handset. The received signal power, measured by the mobile
handsets, is related to their position by nonlinear measurement equations. These consist of a deterministic part,
modeling the received signal power as a function of the position, and a stochastic part, describing model errors
and measurement noise. Additionally, user models are considered, which express knowledge about the motion
of the user of the handset. The new Prior Density Splitting Mixture Estimator (PDSME), a Gaussian mixture
filtering algorithm, significantly improves the localization quality compared to standard filtering techniques as
the Extended Kalman Filter (EKF).

IEEE Signal Processing Letters, 10(5):144-147, May, 2003.

Author : Andreas Rauh, Uwe D. Hanebeck
Title : Calculating Moments of Exponential Densities Using Differential Algebraic Equations
In : IEEE Signal Processing Letters
Date : May 2003

at - Automatisierungstechnik, 51(1):5-12, January, 2003.

Author : Norbert Nitzsche, Uwe D. Hanebeck, Günther Schmidt
Title : Mobile haptische Schnittstellen für weiträumige Telepräsenz: Idee und Methodik
In : at - Automatisierungstechnik
Date : January 2003

Abstract

Dieser Beitrag beschäftigt sich mit Telepräsenzsystemen zur Interaktion mit weiträumigen
realen oder virtuellen Zielumgebungen. Die dafür notwendige Fortbewegung wird durch
freie, gehende Fortbewegung des Anwenders erreicht. Um gleichzeitig haptische Interaktion
mit einer Zielumgebung zu ermöglichen, wird die haptische Schnittstelle mit dem Anwender
mitgeführt. Neuartig ist dabei der Einsatz einer mobilen haptischen Schnittstelle, die der Fortbewegung
des Anwenders aktiv folgt. Basierend auf den dargestellten Methoden wurde ein
prototypisches Telepräsenzsystem zur haptischen Exploration von ausgedehnten virtuellen
Umgebungen entwickelt und getestet.

This article is concerned with telepresence systems for interaction with extended real or virtual
target environments. Locomotion required for this type of interaction is governed by free,
walking locomotion of the operator. In order to allow haptic interaction with the target environment
simultaneously, a haptic interface following the operator is employed. As a novel
approach, we present a mobile haptic interface, which actively follows the operator’s locomotion.
Based on the presented methodology, a prototypic telepresence system for haptic
exploration of extended virtual environments has been implemented and tested.

Publikationen aus dem Jahr 2002

Joachim Horn, Uwe D. Hanebeck, Konrad Riegel, Kai Heesche, Werner Hauptmann,
Nonlinear Set-Theoretic Position Estimation of Cellular Phones,

Ortung und Navigation, Deutsche Gesellschaft für Ortung und Navigation

e.V. (DGON), 1:93-99, 2002.

Author : Joachim Horn, Uwe D. Hanebeck, Konrad Riegel, Kai Heesche, Werner Hauptmann
Title : Nonlinear Set-Theoretic Position Estimation of Cellular Phones
In : Ortung und Navigation, Deutsche Gesellschaft für Ortung und Navigation e.V. (DGON)
Date : 2002

1. SFB-Aussprachetag Human Centered Robotic Systems (HCRS 2002), pp. 83-90, Karlsruhe, Germany, December, 2002.

Author : Norbert Nitzsche, Uwe D. Hanebeck, Günther Schmidt
Title : Extending Telepresent Walking by Motion Compression
In : 1. SFB-Aussprachetag Human Centered Robotic Systems (HCRS 2002)
Date : December 2002

Abstract

Telepresent walking allows visiting remote places
such as museums, exhibitions or industrial sites with a
high degree of realism. While walking freely around in
the user environment, the user sees the remote environment
“through the eyes” of a remote mobile teleoperator.
For that purpose, the user’s motion is tracked
and transferred to the teleoperator. Without additional
processing of the motion data, the size of the remote
environment to be explored is limited to the size of the
user environment. This paper proposes an extension
of telepresent walking to arbitrarily large remote or
virtual spaces based on compressing wide area motion
into the available user space. Motion Compression is
a novel technique and does not make use of scaling or
walking-in-place metaphors. Turning angles and travel
distances are mapped with ratio 1:1.

Habilitationsschrift, Fakultät für Elektrotechnik und Informationstechnik, Technische Universität München, Referent: G. Schmidt, Korreferent: V. Krebs, September, 2002.

Author : Uwe D. Hanebeck
Title : Nonlinear Methods for State Estimation in Stochastic Dynamic Systems -- A Concise Introduction
In : Habilitationsschrift, Fakultät für Elektrotechnik und Informationstechnik, Technische Universität München, Referent: G. Schmidt, Korreferent: V. Krebs
Date : September 2002

Proceedings of the 2002 IEEE International Conference on Robotics and Automation (ICRA 2002), Washington D. C., USA, May, 2002.

Author : Oliver Lorch, Javier F. Seara, Klaus H. Strobl, Uwe D. Hanebeck, Günther Schmidt
Title : Perception Errors in Vision Guided Walking: Analysis, Modeling, and Filtering
In : Proceedings of the 2002 IEEE International Conference on Robotics and Automation (ICRA 2002)
Date : May 2002

Author : Uwe D. Hanebeck, Joachim Horn, Konrad Riegel, Kai Heesche, Werner Hauptmann
Title : Patent: Verfahren und Anordnung sowie Computerprogramm mit Programmcode-Mitteln und Computerprogramm-Produkt zur Ermittlung einer Position einer mobilen Kommunikationseinrichtung in einem Kommunikationsnetz
In :
Date : April 2002

International Symposium on Location Based Services for Cellular Users (LOCELLUS 2002), Munich, Germany, April, 2002.

Author : Joachim Horn, Uwe D. Hanebeck, Konrad Riegel, Kai Heesche, Werner Hauptmann
Title : Nonlinear Set-Theoretic Position Estimation of Cellular Phones
In : International Symposium on Location Based Services for Cellular Users (LOCELLUS 2002)
Date : April 2002

Publikationen aus dem Jahr 2001

Proceedings of the 2001 American Control Conference (ACC 2001), Arlington, Virginia, USA, 2001.

Author : Uwe D. Hanebeck, Joachim Horn
Title : New Estimators for Mixed Stochastic and Set Theoretic Uncertainty Models: The General Case
In : Proceedings of the 2001 American Control Conference (ACC 2001)
Date : 2001

Proceedings of the 2001 European Control Conference (ECC 2001), Porto, Portugal, September, 2001.

Author : Uwe D. Hanebeck
Title : A Square-Root Algorithm for Set Theoretic State Estimation
In : Proceedings of the 2001 European Control Conference (ECC 2001)
Date : September 2001

Abstract

This paper presents a modified set theoretic framework
for estimating the state of a linear dynamic system based
on uncertain measurements. The measurement errors are
assumed to be unknown but bounded by ellipsoidal sets.
Based on this assumption, a recursive state estimator is
(re–)derived in a tutorial fashion. It comprises both the
prediction step (time update), i.e., propagation of a set
of feasible states by means of the system model and the
filter step (measurement update), i.e., inclusion of a new
measurement into the current estimate. The main contribution
is an efficient square–root formulation of this
estimator, which is well suited especially for practical
applications.

Proceedings of the 2001 European Control Conference (ECC 2001), Porto, Portugal, September, 2001.

Author : Uwe D. Hanebeck, Joachim Horn
Title : Filtering in the Presence of Uncertainties with Known Bounds and Random Noise with Known Distribution
In : Proceedings of the 2001 European Control Conference (ECC 2001)
Date : September 2001

Proceedings of the 2001 European Control Conference (ECC 2001), Porto, Portugal, September, 2001.

Author : Uwe D. Hanebeck, Joachim Horn
Title : New Estimators for Mixed Stochastic and Set Theoretic Uncertainty Models: The General Case
In : Proceedings of the 2001 European Control Conference (ECC 2001)
Date : September 2001

Abstract

New filters are derived for estimating the n–dimensional
state of a linear dynamic system based on uncertain m–
dimensional observations, which suffer from two types of
uncertainties simultaneously. The first uncertainty is a
stochastic process with given distribution. The second uncertainty
is only known to be bounded, the exact underlying
distribution is unknown. The new estimators combine
set theoretic and stochastic estimation in a rigorous manner
and provide a continuous transition between the two
classical estimation concepts. They converge to a set theoretic
estimator, when the stochastic error goes to zero,
and to a Kalman filter, when the bounded error vanishes.
In the mixed noise case, solution sets are provided that
are uncertain in a stochastic sense.

Proceedings of the 10th IEEE International Workshop on Robot-Human Interactive Communication (ROMAN 2001), Bordeaux/Paris, France, September, 2001.

Author : Norbert Nitzsche, Uwe D. Hanebeck, Günther Schmidt
Title : Mobile Haptic Interaction with Extended Real or Virtual Environments
In : Proceedings of the 10th IEEE International Workshop on Robot-Human Interactive Communication (ROMAN 2001)
Date : September 2001

Proceedings of the 2001 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2001), pp. 159-164, Baden-Baden, Germany, August, 2001.

Author : Uwe D. Hanebeck
Title : Recursive Nonlinear Set-Theoretic Estimation Based on Pseudo-Ellipsoids
In : Proceedings of the 2001 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2001)
Date : August 2001

Proceedings of the 2001 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2001), pp. 147-152, Baden-Baden, Germany, August, 2001.

Author : Uwe D. Hanebeck, Kai Briechle
Title : New Results for Stochastic Prediction and Filtering with Unknown Correlations
In : Proceedings of the 2001 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2001)
Date : August 2001

Abstract

This paper considers state estimation for dynamic
systems in the case of nonwhite, mutually correlated
noise processes. Here, the problem is complicated
by the fact, that only the individual covariances are
known; cross covariances between random variables
obtained by taking individual noise processes at different
time steps and between different noise processes
are completely unknown. New estimator equations
for solving this problem are derived in feedback form
for both the prediction step and for the filtering step
based on existing ideas known as covariance intersection.
Solutions are given for the most general case
of updating an N–dimensional state vector estimate
based on M–dimensional observations. Furthermore,
computationally efficient solutions for obtaining minimum
covariance estimates are derived to avoid numerical
optimization otherwise required.

Proceedings of the 2001 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2001), pp. 85-90, Baden-Baden, Germany, August, 2001.

Author : Uwe D. Hanebeck, Kai Briechle, Joachim Horn
Title : A Tight Bound for the Joint Covariance of Two Random Vectors with Unknown but Constrained Cross-Correlation
In : Proceedings of the 2001 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2001)
Date : August 2001

Abstract

This paper derives a fundamental result for processing
two correlated random vectors with unknown
cross–correlation, where constraints on the maximum
absolute correlation coefficient are given. A tight upper
bound for the joint covariance matrix is derived
on the basis of the individual covariances and the correlation
constraint. For symmetric constraints, the
bounding covariance matrix naturally possesses zero
cross covariances, which further increases their usefulness
in applications. Performance is demonstrated
by recursively propagating a state through a linear
dynamical system suffering from stochastic noise correlated
with the system state.

Proceedings of the 2001 American Control Conference (ACC 2001), Arlington, Virginia, USA, June, 2001.

Author : Uwe D. Hanebeck
Title : Optimal Filtering for Polynomial Measurement Nonlinearities with Additive Non-Gaussian Noise
In : Proceedings of the 2001 American Control Conference (ACC 2001)
Date : June 2001

Tagungsband des GMA-Kongress 2001, Baden-Baden, Germany, May, 2001.

Author : Uwe D. Hanebeck, Joachim Horn
Title : Neue Ergebnisse zur Zustandsschätzung bei simultan auftretenden mengenbasierten und stochastischen Unsicherheiten
In : Tagungsband des GMA-Kongress 2001
Date : May 2001

Proceedings of SPIE, Vol. 4387, AeroSense Symposium, Orlando, Florida, USA, April, 2001.

Author : Kai Briechle, Uwe D. Hanebeck
Title : Template Matching using Fast Normalized Cross Correlation
In : Proceedings of SPIE, Vol. 4387, AeroSense Symposium
Date : April 2001

Abstract

In this paper we present an algorithm for fast calculation
of the normalized cross correlation (NCC) and its application to the problem
of template matching. Given a template t, whose position is to be determined
in an image f, the basic idea of the algorithm is to represent the template,
for which the normalized cross correlation is calculated, as a sum of rectangular
basis functions. Then the correlation is calculated for each basis function
instead of the whole template. The result of the correlation of the template t
and the image f is obtained as the weighted sum of the correlation functions
of the basis functions. Depending on the approximation, the algorithm can by
far outperform Fourier-transform based implementations of the normalized cross
correlation algorithm and it is especially suited to problems, where many
different templates are to be found in the same image f.

Proceedings of SPIE, Vol. 4385, AeroSense Symposium, Orlando, Florida, USA, April, 2001.

Author : Uwe D. Hanebeck, Joachim Horn
Title : An Efficient Method for Simultaneous Map Building and Localization
In : Proceedings of SPIE, Vol. 4385, AeroSense Symposium
Date : April 2001

Abstract

We consider the problem of simultaneously locating an observer and a set of environmental landmarks with respect
to an inertial coordinate system, when both the observer position and the landmark positions are initially uncertain.
For solving this problem, a new state estimator is introduced, which allows the problem to be consistently
solved locally.

Publikationen aus dem Jahr 2000

at - Automatisierungstechnik, 48(6):265-272, 2000.

Author : Uwe D. Hanebeck, Joachim Horn
Title : Zustandsschätzung im Fall simultan auftretender mengenbasierter und stochastischer Unsicherheiten
In : at - Automatisierungstechnik
Date : 2000

Information Fusion, Elsevier Science, 1(1):55-63, 2000.

Author : Uwe D. Hanebeck, Joachim Horn
Title : Fusing Information Simultaneously Corrupted by Uncertainties with Known Bounds and Random Noise with Known Distribution
In : Information Fusion, Elsevier Science
Date : 2000

Autonome Mobile Systeme 2000 (AMS 2000), 16. Fachgespräch, Karlsruhe, Informatik Aktuell, Springer, November, 2000.

Author : Kai Briechle, Uwe D. Hanebeck
Title : Ein Zwei-Karten-Verfahren zur gleichzeitigen Kartographierung und Lokalisierung
In : Autonome Mobile Systeme 2000 (AMS 2000), 16. Fachgespräch, Karlsruhe, Informatik Aktuell
Date : November 2000

Proceedings of the 9th International Workshop on Robotics in Alpe-Adria-Danube Region (RAAD 2000), pp. 11-16, Maribor, Slovenia, June, 2000.

Author : Nihad Šaldić, Uwe D. Hanebeck, Günther Schmidt
Title : Modular Wheel Systems for Omnidirectional Service Robots
In : Proceedings of the 9th International Workshop on Robotics in Alpe-Adria-Danube Region (RAAD 2000)
Date : June 2000

Robotik 2000 Tagung (VDI/VDE-Gesellschaft Mess- und Automatisierungstechnik), VDI Berichte 1552, pp. 39-44, VDI, Berlin, June, 2000.

Author : Uwe D. Hanebeck, Nihad Šaldić, Franz Freyberger, Günther Schmidt
Title : Modulare Radsatzsysteme für omnidirektionale mobile Roboter
In : Robotik 2000 Tagung (VDI/VDE-Gesellschaft Mess- und Automatisierungstechnik), VDI Berichte 1552
Date : June 2000

Publikationen aus dem Jahr 1999

Proceedings of the 1999 IEEE Conference on Decision and Control (CDC 1999), pp. 1934-1939, Phoenix, Arizona, USA, December, 1999.

Author : Uwe D. Hanebeck, Joachim Horn
Title : New Estimators for Mixed Stochastic and Set Theoretic Uncertainty Models: The Scalar Measurement Case
In : Proceedings of the 1999 IEEE Conference on Decision and Control (CDC 1999)
Date : December 1999

Autonome Mobile Systeme 1999 (AMS 1999), 15. Fachgespräch, München, Informatik Aktuell, pp. 200-210, Springer, November, 1999.

Author : Kai Briechle, Uwe D. Hanebeck
Title : Simultane Lokalisierung und Kartenaufbau für einen mobilen Serviceroboter
In : Autonome Mobile Systeme 1999 (AMS 1999), 15. Fachgespräch, München, Informatik Aktuell
Date : November 1999

Proceedings of the 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 1999), Kjongju, Republic of Korea, October, 1999.

Author : Uwe D. Hanebeck, Joachim Horn
Title : A New Concept for State Estimation in the Presence of Stochastic and Set Theoretic Uncertainties
In : Proceedings of the 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 1999)
Date : October 1999

Proceedings of the 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 1999), Kjongju, Republic of Korea, October, 1999.

Author : Uwe D. Hanebeck, Nihad Šaldić
Title : A Modular Wheel System for Mobile Robot Applications
In : Proceedings of the 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 1999)
Date : October 1999

Proceedings of the 1999 IEEE Systems, Man, and Cybernetics Conference (SMC 1999), Tokyo, Japan, October, 1999.

Author : Kai Briechle, Uwe D. Hanebeck
Title : Self-Localization of a Mobile Robot Using a Fast Cross-Correlation Algorithm
In : Proceedings of the 1999 IEEE Systems, Man, and Cybernetics Conference (SMC 1999)
Date : October 1999

Proceedings of the 1999 IEEE Systems, Man, and Cybernetics Conference (SMC 1999), Tokyo, Japan, October, 1999.

Author : Uwe D. Hanebeck, Joachim Horn
Title : New Results for State Estimation in the Presence of Mixed Stochastic and Set Theoretic Uncertainties
In : Proceedings of the 1999 IEEE Systems, Man, and Cybernetics Conference (SMC 1999)
Date : October 1999

Proceedings of the 5th European Control Conference (ECC 1999), Karlsruhe, Germany, September, 1999.

Author : Uwe D. Hanebeck, Joachim Horn
Title : New Estimators for Mixed Stochastic and Set Theoretic Uncertainty Models: The Vector Case
In : Proceedings of the 5th European Control Conference (ECC 1999)
Date : September 1999

Automatica, 35(6):1101-1109, June, 1999.

Author : Uwe D. Hanebeck, Joachim Horn, Günther Schmidt
Title : On Combining Statistical and Set Theoretic Estimation
In : Automatica
Date : June 1999

Proceedings of the 1999 IEEE International Conference on Robotics and Automation (ICRA 1999), pp. 1335-1340, Detroit, Michigan, USA, May, 1999.

Author : Uwe D. Hanebeck, Joachim Horn
Title : A New Estimator for Mixed Stochastic and Set Theoretic Uncertainty Models Applied to Mobile Robot Localization
In : Proceedings of the 1999 IEEE International Conference on Robotics and Automation (ICRA 1999)
Date : May 1999

Proceedings of SPIE, Vol. 3720, AeroSense Symposium, pp. 336-344, Orlando, Florida, USA, April, 1999.

Author : Uwe D. Hanebeck, Joachim Horn
Title : A New State Estimator for a Mixed Stochastic and Set Theoretic Uncertainty Model
In : Proceedings of SPIE, Vol. 3720, AeroSense Symposium
Date : April 1999

Publikationen aus dem Jahr 1998

Experimental Robotics V, The Fifth International Symposium, Barcelona, Catalonia, Lecture Notes in Control and Information Sciences 223, pp. 385-396, Springer, Barcelona, Spain, 1998.

Author : Uwe D. Hanebeck, Günther Schmidt
Title : Mobile Robot Localization Based on Efficient Processing of Sensor Data and Set-theoretic State Estimation
In : Experimental Robotics V, The Fifth International Symposium, Barcelona, Catalonia, Lecture Notes in Control and Information Sciences 223
Date : 1998

Autonome Mobile Systeme 1998 (AMS 1998), 14. Fachgespräch, Karlsruhe, Informatik Aktuell, pp. 164-171, Springer, November, 1998.

Author : Ralf Furtwängler, Uwe D. Hanebeck, Günther Schmidt
Title : Dynamische Bänder zur Bewegungsplanung für mobile Manipulatoren
In : Autonome Mobile Systeme 1998 (AMS 1998), 14. Fachgespräch, Karlsruhe, Informatik Aktuell
Date : November 1998

Proceedings of 7th IEEE International Workshop on Robot and Human Communication (ROMAN 1998), pp. 187-194, Takamatsu, Japan, September, 1998.

Author : Günther Schmidt, Uwe D. Hanebeck
Title : Aspects of Human-Robot-Communication for a Semi-autonomous Robotic Assistant for the Health Care Environment
In : Proceedings of 7th IEEE International Workshop on Robot and Human Communication (ROMAN 1998)
Date : September 1998

Journal of Intelligent and Robotic Systems, Kluwer Academic Publishers, 22(3-4):191-209, July, 1998.

Author : Eugen Ettelt, Ralf Furtwängler, Uwe D. Hanebeck, Günther Schmidt
Title : Design Issues of a Semi-Autonomous Robotic Assistant for the Health Care Environment
In : Journal of Intelligent and Robotic Systems, Kluwer Academic Publishers
Date : July 1998

Proceedings of the 5th International Workshop on Advanced Motion Control (AMC 1998), pp. 440-445, Coimbra, Portugal, June, 1998.

Author : Ralf Furtwängler, Uwe D. Hanebeck, Günther Schmidt
Title : Dynamic Control of a Mobile Manipulator
In : Proceedings of the 5th International Workshop on Advanced Motion Control (AMC 1998)
Date : June 1998

Proceedings of the 1998 IEEE International Conference on Robotics and Automation (ICRA 1998), pp. 2813-2818, Leuven, Belgium, May, 1998.

Author : Uwe D. Hanebeck
Title : Pipelined Sampling Techniques for Sonar Tracking Systems
In : Proceedings of the 1998 IEEE International Conference on Robotics and Automation (ICRA 1998)
Date : May 1998

Publikationen aus dem Jahr 1997

Phdthesis, TU München, Referent: G. Schmidt, Korreferent: E. D. Dickmanns, Fortschrittsberichte VDI, Reihe 8: Meß-, Steuerungs- und Regelungstechnik, Nr. 643, VDI Verlag, Düsseldorf, 1997.

Author : Uwe D. Hanebeck
Title : Lokalisierung eines mobilen Roboters mittels effizienter Auswertung von Sensordaten und mengenbasierter Zustandsschätzung
In : Phdthesis, TU München, Referent: G. Schmidt, Korreferent: E. D. Dickmanns, Fortschrittsberichte VDI, Reihe 8: Meß-, Steuerungs- und Regelungstechnik, Nr. 643, VDI Verlag, Düsseldorf
Date : 1997

International Advanced Robotics Programme (IARP 1997), Proceedings of the Second International Workshop on "Service and Personal Robots: Technologies and Applications", Genova, Italy, October, 1997.

Author : Günther Schmidt, Uwe D. Hanebeck, Christian Fischer
Title : A Mobile Service Robot for the Hospital and Home Environment
In : International Advanced Robotics Programme (IARP 1997), Proceedings of the Second International Workshop on "Service and Personal Robots: Technologies and Applications"
Date : October 1997

Proceedings of the 1997 IEEE/RSJ/GI International Conference on Intelligent Robots and Systems (IROS 1997), pp. 518-525, Grenoble, France, September, 1997.

Author : Uwe D. Hanebeck, Christian Fischer, Günther Schmidt
Title : ROMAN: A Mobile Robotic Assistant for Indoor Service Applications
In : Proceedings of the 1997 IEEE/RSJ/GI International Conference on Intelligent Robots and Systems (IROS 1997)
Date : September 1997

Fifth International Symposium on Experimental Robotics (ISER 1997), pp. 321-332, Barcelona, Spain, June, 1997.

Author : Uwe D. Hanebeck, Günther Schmidt
Title : Mobile Robot Localization Based on Efficient Processing of Sensor Data and Set-theoretic State Estimation
In : Fifth International Symposium on Experimental Robotics (ISER 1997)
Date : June 1997

Publikationen aus dem Jahr 1996

IFAC Control Engineering Practice, Elsevier Science, 4(8):1109-1118, 1996.

Author : Uwe D. Hanebeck, Günther Schmidt
Title : Localization of Fast Mobile Robots Based on an Advanced Angle-Measurement Technique
In : IFAC Control Engineering Practice, Elsevier Science
Date : 1996

Journal of Fuzzy Sets and Systems, Special Issue on Neuro-Fuzzy Techniques and Applications, Elsevier Science, 79:59-68, 1996.

Author : Uwe D. Hanebeck, Günther Schmidt
Title : Genetic Optimization of Fuzzy Networks
In : Journal of Fuzzy Sets and Systems, Special Issue on Neuro-Fuzzy Techniques and Applications, Elsevier Science
Date : 1996

Autonome Mobile Systeme 1996 (AMS 1996), 12. Fachgespräch, München, Informatik aktuell, pp. 314-333, Springer, October, 1996.

Author : Wolfgang Daxwanger, Eugen Ettelt, Christian Fischer, Franz Freyberger, Uwe D. Hanebeck, Günther Schmidt
Title : ROMAN: Ein mobiler Serviceroboter als persönlicher Assistent in belebten Innenräumen
In : Autonome Mobile Systeme 1996 (AMS 1996), 12. Fachgespräch, München, Informatik aktuell
Date : October 1996

Proceedings of the 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 1996), pp. 3070-3073, Atlanta, Georgia, USA, May, 1996.

Author : Uwe D. Hanebeck, Günther Schmidt
Title : Closed-Form Elliptic Location with an Arbitrary Array Topology
In : Proceedings of the 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 1996)
Date : May 1996

Proceedings of the 1996 IEEE International Conference on Robotics and Automation (ICRA 1996), pp. 3081-3086, Minneapolis, Minnesota, USA, April, 1996.

Author : Uwe D. Hanebeck, Joachim Horn, Günther Schmidt
Title : On Combining Set Theoretic and Bayesian Estimation
In : Proceedings of the 1996 IEEE International Conference on Robotics and Automation (ICRA 1996)
Date : April 1996

Proceedings of the 1996 IEEE International Conference on Robotics and Automation (ICRA 1996), pp. 1387-1394, Minneapolis, Minnesota, USA, April, 1996.

Author : Uwe D. Hanebeck, Günther Schmidt
Title : Set theoretic Localization of Fast Mobile Robots Using an Angle Measurement Technique
In : Proceedings of the 1996 IEEE International Conference on Robotics and Automation (ICRA 1996)
Date : April 1996

Publikationen aus dem Jahr 1995

Intelligent Robots and Systems, pp. 1-14, Elsevier Science, 1995.

Author : Uwe D. Hanebeck, Günther Schmidt
Title : A New High Performance Multisonar System for Fast Mobile Robot Applications
In : Intelligent Robots and Systems
Date : 1995

Autonome Mobile Systeme 1995 (AMS 1995), 11. Fachgespräch, Karlsruhe, Informatik Aktuell, pp. 162-171, Springer, November, 1995.

Author : Uwe D. Hanebeck, Günther Schmidt
Title : Schnelle Objektdetektion mit Ultraschallsensor-Arrays
In : Autonome Mobile Systeme 1995 (AMS 1995), 11. Fachgespräch, Karlsruhe, Informatik Aktuell
Date : November 1995

IFAC Workshop on Intelligent Components for Autonomous and Semi-Autonomous Vehicles (ICASAV 1995), pp. 87-92, Toulouse, France, October, 1995.

Author : Uwe D. Hanebeck, Günther Schmidt
Title : Absolute Localization of Fast Mobile Robots Based on an Angle Measurement Technique
In : IFAC Workshop on Intelligent Components for Autonomous and Semi-Autonomous Vehicles (ICASAV 1995)
Date : October 1995

19th Annual German Conference on Artificial Intelligence (KI'95), Bielefeld, KI-95 Activities: Workshops, Posters, Demos, pp. 164-166, Gesellschaft für Informatik e.V., Bielefeld, Germany, September, 1995.

Author : Uwe D. Hanebeck, Günther K. Schmidt
Title : Absolute Localization of Fast Mobile Robots Based on an Angle Measurement Technique
In : 19th Annual German Conference on Artificial Intelligence (KI'95), Bielefeld, KI-95 Activities: Workshops, Posters, Demos
Date : September 1995

Proceedings of the TELEMAN Student Research Projects Congress, pp. 95-100, Noordwijkerhout, Netherlands, July, 1995.

Author : Stefan Maier, Uwe D. Hanebeck
Title : Fuzzy Guidance of Omnidirectional Mobile Robots Including Sensor-Based Obstacle Avoidance
In : Proceedings of the TELEMAN Student Research Projects Congress
Date : July 1995

Publikationen aus dem Jahr 1994

Proceedings of the 1994 International Conference on Neural Information Processing (ICONIP 1994), pp. 1583-1588, Seoul, Republic of Korea, October, 1994.

Author : Uwe D. Hanebeck, Günther K. Schmidt
Title : Optimization of Fuzzy Networks via Genetic Algorithms
In : Proceedings of the 1994 International Conference on Neural Information Processing (ICONIP 1994)
Date : October 1994

Proceedings of the 2nd European Congress on Intelligent Techniques and Soft Computing (EUFIT 1994), pp. 1011-1013, Aachen, Germany, September, 1994.

Author : Uwe D. Hanebeck, Günther Schmidt
Title : Optimization of Fuzzy Networks via Genetic Algorithms
In : Proceedings of the 2nd European Congress on Intelligent Techniques and Soft Computing (EUFIT 1994)
Date : September 1994

Proceedings of the 1994 IEEE/RSJ/GI International Conference on Intelligent Robots and Systems (IROS 1994), pp. 1853-1860, Munich, Germany, September, 1994.

Author : Uwe D. Hanebeck, Günther Schmidt
Title : A New High Performance Multisonar System for Fast Mobile Robots
In : Proceedings of the 1994 IEEE/RSJ/GI International Conference on Intelligent Robots and Systems (IROS 1994)
Date : September 1994

Publikationen aus dem Jahr 1993

at - Automatisierungstechnik, 41(8):300-306, 1993.

Author : Uwe D. Hanebeck, Günther Schmidt
Title : Experimentelle Vertiefung der Ausbildung im Fuzzy-Logik-Reglerentwurf
In : at - Automatisierungstechnik
Date : 1993

Proceedings of the 12th World Congress of the International Federation of Automatic Control (IFAC 1993), pp. 147-150, Sydney, Australia, July, 1993.

Author : Günther Schmidt, Uwe D. Hanebeck
Title : Teaching Fuzzy Logic Control Design Through Laboratory Experiments and Student Projects
In : Proceedings of the 12th World Congress of the International Federation of Automatic Control (IFAC 1993)
Date : July 1993