text:
formalizes object silhouettes from image data
               as measurements within a Bayesian estimation framework. Projecting
               object silhouettes from images back into space yields a visual hull
               that constrains the object. In this work, we focus on the 2D case.
               We derive a general equation for the silhouette measurement update
               that explicitly considers segmentation uncertainty of each pixel.
               By assuming a bounded error for the silhouettes, we can reduce the
               complexity of the general solution to only consider uncertain edges
               and derive an approximate measurement update. In simulations, we
               show that the proposed approach dramatically improves point-cloud-based
               estimators, especially in the presence of high noise.},
  pdf       = {Fusion13_Faion.pdf},
  month     = jul
}

@inproceedings{Fusion13_Gilitschenski,
  author    = {Igor Gilitschenski and Gerhard Kurz and Uwe D. Hanebeck},
  title     = {{Bearings-Only Sensor Scheduling Using Circular Statistics}},
  booktitle = {Proceedings of the 16th International Conference on Information Fusion (Fusion 2013)},
  year      = {2013},
  address   = {Istanbul, Turkey},
  abstract  = {In this paper, we introduce a novel approach for scheduled tracking
               of a moving target based on bearings-only sensors. Unlike classical
               approaches that are typically based on the extended or unscented
               Kalman filter, we rely on circular statistics to describe probability
               distributions for angular measurements more accurately. As the energy
               available to sensors is limited in many scenarios, we introduce a
               scheduling algorithm that selects a subset of two sensors to be active
               at any given time step while minimizing the uncertainty of the state
               estimate. This is done by anticipating possible future measurements.
               We evaluate the proposed method in simulations and compare it to
               an UKF-based solution. Our evaluation demonstrates the superiority
               of the presented approach, particularly when high measurement uncertainty
               makes consideration of the circular geometry necessary.},
  pdf       = {Fusion13_Gilitschenski.pdf},
  month     = jul
}

@inproceedings{Fusion13_Hanebeck,
  author    = {Uwe D. Hanebeck},
  title     = {{PGF 42: Progressive Gaussian Filtering with a Twist}},
  booktitle = {Proceedings of the 16th International Conference on Information Fusion (Fusion 2013)},
  year      = {2013},
  address   = {Istanbul, Turkey},
  pdf       = {Fusion13_Hanebeck.pdf},
  abstract  = {A new Gaussian filter for estimating the state of nonlinear systems
               is derived that relies on two main ingredients: i) the progressive
               inclusion of the measurement information and ii) a tight coupling
               between a Gaussian density and its deterministic Dirac mixture approximation.
               No second Gaussian assumption for the joint density of state and
               measurement is required, so that the performance is much better than
               that of Linear Regression Kalman Filters (LRKFs), which heavily
               rely on this assumption. In addition, the new filter directly works
               with the generative system description. No Likelihood function is
               required. It can be used as a plug -in replacement for standard Gaussian
               filters such as the UKF.},
  month     = jul
}

@inproceedings{Fusion13_Huber,
  author    = {Marco F. Huber and Uwe D. Hanebeck},
  title     = {{Gaussian Filtering for Polynomial Systems Based on Moment Homotopy}},
  booktitle = {Proceedings of the 16th International Conference on Information Fusion (Fusion 2013)},
  year      = {2013},
  address   = {Istanbul, Turkey},
  abstract  = {This paper proposes Gaussian filters for polynomial systems with efficient
               solutions for both the prediction and the filter step. For the prediction
               step, computationally efficient closed-form solutions are derived
               for calculating the exact moments. In order to achieve a higher estimation
               quality, the filter step is solved without the usual additional assumption
               that state and measurement are jointly Gaussian distributed. As this
               significantly complicates the required moment calculation, a homotopy
               continuation method is employed that yields almost optimal results.},
  pdf       = {Fusion13_Huber.pdf},
  month     = jul
}

@inproceedings{Fusion13_Kurz-Bingham,
  author    = {Gerhard Kurz and Igor Gilitschenski and Simon J. Julier and Uwe D. Hanebeck},
  title     = {{Recursive Estimation of Orientation Based on the Bingham Distribution}},
  booktitle = {Proceedings of the 16th International Conference on Information Fusion (Fusion 2013)},
  year      = {2013},
  address   = {Istanbul, Turkey},
  abstract  = {Directional estimation is a common problem in many tracking applications.
               Traditional filters like the Kalman filter perform poorly because
               they fail to take the periodic nature of the problem into account.
               We present a recursive filter for directional data based on the Bingham
               distribution in two dimensions. The proposed filter can be applied
               to circular filtering problems with 180 degree symmetry, i.e., rotations
               by 180 degrees cannot be distinguished. It is easily implemented
               using standard numerical techniques and suitable for real-time applications.
               The presented approach is extensible to quaternions, which allow
               tracking arbitrary three-dimensional orientations. We evaluate our
               filter in a challenging scenario and compare it to a traditional
               Kalman filtering approach.},
  pdf       = {Fusion13_Kurz-Bingham.pdf},
  annote    = {Best Student Paper Award First Runner-Up Certificate (PDF)},
  month     = jul
}

@inproceedings{Fusion13_Kurz,
  author    = {Gerhard Kurz and Uwe D. Hanebeck},
  title     = {{Recursive Fusion of Noisy Depth and Position Measurements for Surface Reconstruction}},
  booktitle = {Proceedings of the 16th International Conference on Information Fusion (Fusion 2013)},
  year      = {2013},
  address   = {Istanbul, Turkey},
  pdf       = {Fusion13_Kurz.pdf},
  abstract  = {We propose an algorithm to combine both depth and position measurements
               when estimating a continuous surface. Position measurements originate
               from a fixed point on the surface, whereas depth measurements are
               determined by the intersection of the surface with a line originating
               from the depth sensor. Through fusion of both types of measurements,
               it is possible to benefit from the advantages of different sensors.
               The surface is obtained through interpolation of control points with
               splines, which allows a compact representation of the surface. In
               order to simplify the problem of intersecting the surface with lines
               originating from the depth sensor, we propose the use of polar or
               spherical coordinates in surface parameterization. The presented
               algorithm can be applied in both 2D and 3D settings and is independent
               of the particular choice of sensors. Our method can recursively include
               new information as it is obtained by using nonlinear filtering and
               it considers uncertainties associated with the measurements.},
  annote    = {Winner Best Paper Award Certificate (PDF)},
  month     = jul
}

@inproceedings{Fusion13_Noack,
  author    = {Benjamin Noack and Simon J. Julier and Marc Reinhardt and Uwe D. Hanebeck},
  title     = {{Nonlinear Federated Filtering}},
  booktitle = {Proceedings of the 16th International Conference on Information Fusion (Fusion 2013)},
  year      = {2013},
  address   = {Istanbul, Turkey},
  abstract  = {The federated Kalman filter embodies an efficient and easy-to-implement
               solution for linear distributed estimation problems. Data from independent
               sensors can be processed locally and in parallel on different nodes
               without running the risk of erroneously ignoring possible dependencies.
               The underlying idea is to counteract the common process noise issue
               by inflating the joint process noise matrix. In this paper, the same
               trick is generalized to nonlinear models and non-Gaussian process
               noise. The probability density of the joint process noise is split
               into an exponential mixture of transition densities. By this means,
               the process noise is modeled to independently affect the local system
               models. The estimation results provided by the sensor devices can
               then be fused, just as if they were indeed independent.},
  pdf       = {Fusion13_Noack.pdf},
  month     = jul
}

@inproceedings{Fusion13_Pfaff,
  author    = {Florian Pfaff and Benjamin Noack and Uwe D. Hanebeck},
  title     = {{Data Validation in the Presence of Stochastic and Set-Membership Uncertainties}},
  booktitle = {Proceedings of the 16th International Conference on Information Fusion (Fusion 2013)},
  year      = {2013},
  address   = {Istanbul, Turkey},
  abstract  = {For systems suffering from different types of uncertainties, finding
               criteria for validating measurements can be challenging. In this
               paper, we regard both stochastic Gaussian noise with full or imprecise
               knowledge about correlations and unknown but bounded errors. The
               validation problems arising in the individual and combined cases
               are illustrated to convey different perspectives on the proposed
               conditions. Furthermore, hints are provided for the algorithmic implementation
               of the validation tests. Particular focus is put on ensuring a predefined
               lower bound for the probability of correctly classifying valid data.},
  pdf       = {Fusion13_Pfaff.pdf},
  month     = jul
}

@inproceedings{Fusion13_Reinhardt,
  author    = {Marc Reinhardt and Benjamin Noack and Uwe D. Hanebeck},
  title     = {{Advances in Hypothesizing Distributed Kalman Filtering}},
  booktitle = {Proceedings of the 16th International Conference on Information Fusion (Fusion 2013)},
  year      = {2013},
  address   = {Istanbul, Turkey},
  abstract  = {In this paper, linear distributed estimation is revisited on the basis
               of the hypothesizing distributed Kalman filter and equations for
               a flexible application of the algorithm are derived. We propose a
               new approximation for the mean-squared-error matrix and present techniques
               for automatically improving the hypothesis about the global measurement
               model. Utilizing these extensions, the precision of the filter is
               improved so that it asymptotically yields optimal results for time-invariant
               models. Pseudo-code for the implementation of the algorithm is provided
               and the lossless inclusion of out-of-sequence measurements is discussed.
               An evaluation demonstrates the effect of the new extensions and compares
               the results to state-of-the-art methods.},
  pdf       = {Fusion13_Reinhardt.pdf},
  month     = jul
}

@inproceedings{Fusion13_Sijs,
  author    = {Joris Sijs and Benjamin Noack and Uwe D. Hanebeck},
  title     = {{Event-based State Estimation with Negative Information}},
  booktitle = {Proceedings of the 16th International Conference on Information Fusion (Fusion 2013)},
  year      = {2013},
  address   = {Istanbul, Turkey},
  abstract  = {To reduce the amount of data transfer in networked systems, measurements
               are usually taken only when an event occurs rather than periodically
               in time. However, this complicates estimation problems considerably
               as it is not guaranteed that new sensor data will be sampled. Therefore,
               an existing state estimator is extended so to cope with event-based
               measurements successfully, i.e., curtail any diverging behavior in
               the estimation results. To that extent, a general formulation of
               event sampling is proposed. This formulation is used to set up a
               state estimator combining stochastic as well as set-membership measurement
               information according to a hybrid update: when an event occurs the
               estimated state is updated using the stochastic measurement received
               (positive information), while at periodic time instants no measurement
               is received (negative information) and the update is based on knowledge
               that the sensor value lies within a bounded subset of the measurement
               space. An illustrative example further shows that the developed estimator
               has an improved representation of estimation errors compared to a
               purely stochastic estimator for various event sampling strategies.},
  pdf       = {Fusion13_Sijs.pdf},
  month     = jul
}

@inproceedings{Fusion13_Steinbring,
  author    = {Jannik Steinbring and Uwe D. Hanebeck},
  title     = {{S2KF: The Smart Sampling Kalman Filter}},
  booktitle = {Proceedings of the 16th International Conference on Information Fusion (Fusion 2013)},
  year      = {2013},
  address   = {Istanbul, Turkey},
  abstract  = {An accurate Linear Regression Kalman Filter (LRKF) for nonlinear systems
               called Smart Sampling Kalman Filter (S²KF) is introduced. It is based
               on a new low-discrepancy Dirac Mixture approximation of Gaussian
               densities. The approximation comprises an arbitrary number of optimally
               and deterministically placed samples in the entire state space, so
               that the filter resolution can be adapted to either achieve high-quality
               results or meet computational constraints. For two samples per dimension,
               the S²KF comprises the UKF as a special case. With an increasing
               number of samples, the new filter quickly converges to the (typically
               infeasible) exact analytic LRKF. The S²KF can be seen as the ultimate
               generalization of all sample-based LRKFs such as the UKF, sigma-point
               filters, higher-order variants etc., as it homogeneously covers the
               state space with an arbitrary number of samples. It is evaluated
               by performing extended target tracking.},
  pdf       = {Fusion13_Steinbring.pdf},
  month     = jul
}

@inproceedings{Fusion13_Zea,
  author    = {Antonio Zea and Florian Faion and Marcus Baum and Uwe D. Hanebeck},
  title     = {{Level-Set Random Hypersurface Models for Tracking Non-Convex Extended Objects}},
  booktitle = {Proceedings of the 16th International Conference on Information Fusion (Fusion 2013)},
  year      = {2013},
  address   = {Istanbul, Turkey},
  abstract  = {This paper presents a novel approach to track a non-convex shape approximation
               of an extended target based on noisy point measurements. For this
               purpose, a novel type of Random Hypersurface Model (RHM), called
               Level-Set RHM is introduced that models the interior of a shape with
               level-sets of an implicit function. Based on the Level-Set RHM, a
               nonlinear measurement equation can be derived that allows to employ
               a standard Gaussian state estimator for tracking an extended object
               even in scenarios with high measurement noise. In this paper, shapes
               are described using polygons and shape regularization is applied
               using ideas from active contour models.},
  pdf       = {Fusion13_Zea.pdf},
  month     = jul
}

@article{AT13_Fischer,
  author   = {J{\"{o}}rg Fischer and Marc Reinhardt and Uwe D. Hanebeck},
  title    = {{Optimal Sequence-Based Control and Estimation of Networked Linear Systems}},
  journal  = {at -- Automatisierungstechnik, Oldenbourg Verlag},
  volume   = {61},
  number   = {7},
  pages    = {467--476},
  year     = {2013},
  doi      = {10.1524/auto.2013.0039},
  url      = {https://dx.doi.org/10.1524/auto.2013.0039},
  abstract = {In this paper, a unified approach to sequence-based control and estimation
              of linear networked systems with multiple sensors is proposed. Time
              delays and data losses in the controller-actuator link are compensated
              by sending sequences of control inputs. The sequence-based design
              paradigm is further extended to the sensor-controller connections
              without increasing the load of the network. In this context, we present
              a recursive solution based on the Hypothesizing Distributed Kalman
              Filter (HKF) that is included in the overall sequence-based controller
              design.},
  pdf      = {AT13_Fischer.pdf},
  month    = jul
}

@inproceedings{ACC13_Fischer,
  author    = {J{\"{o}}rg Fischer and Achim Hekler and Maxim Dolgov and Uwe D. Hanebeck},
  title     = {{Optimal Sequence-Based LQG Control over TCP-like Networks Subject to Random Transmission Delays and Packet Losses}},
  booktitle = {Proceedings of the 2013 American Control Conference (ACC 2013)},
  year      = {2013},
  address   = {Washington D.C., USA},
  pdf       = {ACC13_Fischer.pdf},
  abstract  = {This paper addresses the problem of sequence-based controller design
               for Networked Control Systems (NCS), where control inputs and measurements
               are transmitted over TCP-like network connections that are subject
               to random transmission delays and packet losses. To cope with the
               network effects, the controller not only sends the current control
               input to the actuator, but also a sequence of predicted control inputs
               at every time step. In this setup, we derive an optimal solution
               to the Linear Quadratic Gaussian (LQG) control problem and prove
               that the separation principle holds. Simulations demonstrate the
               improved performance of this optimal controller compared to other
               sequence-based approaches.},
  month     = jun
}

@inproceedings{ACC13_Gilitschenski,
  author    = {Igor Gilitschenski and Uwe D. Hanebeck},
  title     = {{Efficient Deterministic Dirac Mixture Approximation}},
  booktitle = {Proceedings of the 2013 American Control Conference (ACC 2013)},
  year      = {2013},
  address   = {Washington D.C., USA},
  pdf       = {ACC13_Gilitschenski.pdf},
  abstract  = {We propose an efficient method for approximating arbitrary Gaussian
               densities by a mixture of Dirac components. This approach is based
               on the modification of the classical Cramér-von Mises distance, which
               is adapted to the multivariate scenario by using Localized Cumulative
               Distributions (LCDs) as a replacement for the cumulative distribution
               function. LCDs consider the local probabilistic influence of aprobability
               density around a given point. Our modification of the Cramér-von
               Mises distance can be approximated for certain special cases in closed-form.
               The created measure is minimized in order to compute the positions
               of the Dirac components for a standard normal distribution.},
  month     = jun
}

@inproceedings{ACC13_Kurz,
  author    = {Gerhard Kurz and Igor Gilitschenski and Uwe D. Hanebeck},
  title     = {{Recursive Nonlinear Filtering for Angular Data Based on Circular Distributions}},
  booktitle = {Proceedings of the 2013 American Control Conference (ACC 2013)},
  year      = {2013},
  address   = {Washington D.C., USA},
  pdf       = {ACC13_Kurz.pdf},
  abstract  = {Estimation of circular quantities is a widespread problem that occurs
               in many tracking and control applications. Commonly used approaches
               such as the Kalman filter, the extended Kalman filter (EKF), and
               the unscented Kalman filter (UKF) do not take periodicity explicitly
               into account, which can result in low estimation accuracy. We present
               a filtering algorithm for angular quantities in nonlinear systems
               that is based on circular statistics. The new filter switches between
               three different representations of probability distributions on the
               circle, the wrapped normal, the von Mises, and a Dirac mixture density.
               It can be seen as a systematic generalization of the UKF to circular
               statistics. We evaluate the proposed filter in simulations and show
               its superiority to conventional approaches.},
  month     = jun
}

@article{TAES13_Baum_RHM,
  author  = {Marcus Baum and Uwe D. Hanebeck},
  title   = {{Extended Object Tracking with Random Hypersurface Models}},
  journal = {IEEE Transactions on Aerospace and Electronic Systems},
  year    = {2014},
  volume  = {50},
  pages   = {149-159},
  pdf     = {TAES13_Baum.pdf},
  doi     = {10.1109/TAES.2013.120107},
  month   = jan
}

@inproceedings{CDC12_Baum,
  author    = {Marcus Baum and Patrick Ruoff and Dominik Itte and Uwe D. Hanebeck},
  title     = {{Optimal Point Estimates for Multi-target States based on Kernel Distances}},
  booktitle = {Proceedings of the 51st IEEE Conference on Decision and Control (CDC 2012)},
  year      = {2012},
  address   = {Maui, Hawaii, USA},
  abstract  = {Almost all multi-target tracking systems have to generate point estimates
               for the targets, e.g., for displaying the tracks. The novel idea
               in this paper is to  consider point estimates for multi-target states
               that are optimal according to a  kernel distance measure. Because
               the kernel distance is a metric on point sets and ignores the target
               labels, shortcomings of  Minimum Mean Squared Error (MMSE) estimates
               for multi-target states can be avoided. We show how the calculation
               of  these point estimates can be casted as an optimization problem
               and it turns out that it corresponds to the problem of reducing the
               Probability Hypothesis Density (PHD) function   to a Dirac mixture
               density. Finally, we  discuss a generalization of the kernel distance
               called LCD distance, which does not require to choose a specific
               kernel width. The presented methods are evaluated in a  Multiple-Hypotheses
               Tracker (MHT) setting with up to ten targets.},
  pdf       = {CDC12_Baum.pdf},
  month     = dec
}

@inproceedings{CDC12_Chlebek,
  author    = {Christof Chlebek and Achim Hekler and Uwe D. Hanebeck},
  title     = {{Stochastic Nonlinear Model Predictive Control Based on Progressive Density Simplification}},
  booktitle = {Proceedings of the 51st IEEE Conference on Decision and Control (CDC 2012)},
  year      = {2012},
  address   = {Maui, Hawaii, USA},
  abstract  = {Increasing demand for Nonlinear Model Predictive Control with the
               ability to handle highly noise-corrupted systems has recently given
               rise to stochastic control approaches. Besides providing high-quality
               results within a noisy environment, these approaches have one problem
               in common, namely a high computational demand and, as a consequence,
               generally a short prediction horizon. In this paper, we propose to
               reduce the computational complexity of prediction and value function
               evaluation within the control horizon by simplifying the system progressively
               down to the deterministic case. Approximation of occurring probability
               densities by a specific representation, the deterministic Dirac mixture
               density, with a decreasing resolution (i.e., approximation quality)
               leads via natural decomposition to a point estimate and thus, can
               be treated in a deterministic manner. Hence, calculation of the remaining
               time steps requires considerably less computation time.},
  pdf       = {CDC12_Chlebek.pdf},
  month     = dec
}

@inproceedings{CDC12_Hekler,
  author    = {Achim Hekler and J{\"{o}}rg Fischer and Uwe D. Hanebeck},
  title     = {{Sequence-Based Control for Networked Control Systems Based on Virtual Control Inputs}},
  booktitle = {Proceedings of the 51st IEEE Conference on Decision and Control (CDC 2012)},
  year      = {2012},
  address   = {Maui, Hawaii, USA},
  abstract  = {In this paper, we address the problem of controlling a system over
               an unreliable UDP-like network that is affected by time-varying delays
               and randomly occurring packet losses. A major challenge of this setup
               is that the controller just has uncertain information about the control
               inputs actually applied by the actuator. The key idea of this work
               is to model the uncertain control inputs by random variables, the
               so-called virtual control inputs, which are characterized by discrete
               probability density functions. Subject to this probabilistic description,
               a novel, easy to implement sequencebased control approach is proposed
               that extends any given state feedback controller designed without
               consideration of the network-induced disturbances. The high performance
               of the proposed controller is demonstrated by means of Monte Carlo
               simulation runs with an inverted pendulum on a cart.},
  pdf       = {CDC12_Hekler.pdf},
  month     = dec
}

@inproceedings{CDC12_Noack,
  author    = {Benjamin Noack and Florian Pfaff and Uwe D. Hanebeck},
  title     = {{Optimal Kalman Gains for Combined Stochastic and Set-Membership State Estimation}},
  booktitle = {Proceedings of the 51st IEEE Conference on Decision and Control (CDC 2012)},
  year      = {2012},
  address   = {Maui, Hawaii, USA},
  abstract  = {In state estimation theory, two directions are mainly followed in
               order to model disturbances and errors. Either uncertainties are
               modeled as stochastic quantities or they are characterized by their
               membership to a set. Both approaches have distinct advantages and
               disadvantages making each one inherently better suited to model different
               sources of estimation uncertainty. This paper is dedicated to the
               task of combining stochastic and set-membership estimation methods.
               A Kalman gain is derived that minimizes the mean squared error in
               the presence of both stochastic and additional unknown but bounded
               uncertainties, which are represented by Gaussian random variables
               and ellipsoidal sets, respectively. As a result, a generalization
               of the well-known Kalman filtering scheme is attained that reduces
               to the standard Kalman filter in the absence of set-membership uncertainty
               and that otherwise becomes the intersection of sets in case of vanishing
               stochastic uncertainty. The proposed concept also allows to prioritize
               either the minimization of the stochastic uncertainty or the minimization
               of the set-membership uncertainty.},
  pdf       = {CDC12_Noack.pdf},
  month     = dec
}

@inproceedings{CDC12_Reinhardt,
  author    = {Marc Reinhardt and Benjamin Noack and Uwe D. Hanebeck},
  title     = {{Decentralized Control Based on Globally Optimal Estimation}},
  booktitle = {Proceedings of the 51st IEEE Conference on Decision and Control (CDC 2012)},
  year      = {2012},
  address   = {Maui, Hawaii, USA},
  abstract  = {A new method for globally optimal estimation in decentralized sensor-networks
               is applied to the decentralized control problem. The resulting approach
               is proven to be optimal when the nodes have access to all information
               in the network. More precisely, we utilize an algorithm for optimal
               distributed estimation in order to obtain local estimates whose combination
               yields the globally optimal estimate. When the interconnectivity
               is high, the local estimates are almost optimal, which motivates
               the application of the principle of separation. Thus, we optimize
               the controller and finally obtain a flexible algorithm, whose quality
               is evaluated in different scenarios. In applications where the strong
               requirements on a perfect communication cannot be guaranteed, we
               derive quality bounds by help of a detailed evaluation of the algorithm.
               When information is regularly exchanged, it is demonstrated that
               the algorithm performs almost optimally and therefore, offers system
               designers a flexible and easy to implement approach. The field of
               applications lies within the area of strongly networked systems,
               in particular, when communication disturbances cannot be foreseen
               or when the network structure is too complicated to apply optimized
               regulators.},
  pdf       = {CDC12_Reinhardt.pdf},
  month     = dec
}

@inproceedings{CURAC12_Kurz,
  author    = {Gerhard Kurz and Uwe D. Hanebeck},
  title     = {{Image Stabilization with Model-Based Tracking for Beating Heart Surgery}},
  booktitle = {11. Jahrestagung der Deutschen Gesellschaft für Computer- und Roboterassistierte Chirurgie (CURAC12)},
  year      = {2012},
  address   = {D{\"{u}}sseldorf, Germany},
  month     = nov,
  abstract  = {Performing surgery on the beating heart has significant advantages
               compared to cardiopulmonary bypass. However, when performed directly,
               it is very demanding for the surgeon. As an alternative, using a
               teleoperated robot for compensating the heart motion has been proposed.
               As an addition, this paper describes how stabilized images are obtained
               to create the illusion of operating on a stationary heart. For that
               purpose, the heart motion is tracked with a stochastic physical model.
               Based on correspondences obtained by motion tracking, image stabilization
               is considered as a scattered data interpolation problem. The proposed
               algorithms are evaluated on a heart phantom and in in-vivo experiments
               on a porcine heart, which show that there is very little residual
               motion in the stabilized images.},
  pdf       = {CURAC12_Kurz.pdf}
}

@article{TAES12_Baum,
  author   = {Marcus Baum and Uwe D. Hanebeck},
  title    = {{Extended Object Tracking Based on Set-Theoretic and Stochastic Fusion}},
  journal  = {IEEE Transactions on Aerospace and Electronic Systems},
  year     = {2012},
  volume   = {48},
  pages    = {3103--3115},
  number   = {4},
  month    = oct,
  abstract = {A novel approach for extended object tracking is presented. In contrast
              to existing approaches, no statistical assumptions about the location
              of the measurement sources on the extended target object are made.
              As a consequence, a combined set-theoretic and stochastic estimator
              is obtained that is robust to systematic errors in the target model.
              The benefits of the new approach is demonstrated by means of simulations.},
  doi      = {10.1109/TAES.2012.6324680},
  url      = {https://dx.doi.org/10.1109/TAES.2012.6324680},
  issn     = {0018-9251},
  pdf      = {TAES12_Baum.pdf}
}

@inproceedings{IROS12_Faion,
  author    = {Florian Faion and Simon Friedberger and Antonio Zea and Uwe D. Hanebeck},
  title     = {{Intelligent Sensor-Scheduling for Multi-Kinect-Tracking}},
  booktitle = {Proceedings of the 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2012)},
  year      = {2012},
  address   = {Vilamoura, Algarve, Portugal},
  month     = oct,
  abstract  = {This paper describes a method to intelligently schedule a network
               of multiple RGBD sensors in a Bayesian object tracking scenario,
               with special focus on Microsoft Kinect devices. These setups have
               issues such as the large amount of raw data generated by the sensors
               and interference caused by overlapping fields of view. The proposed
               algorithm addresses these issues by selecting and exclusively activating
               the sensor that yields the best measurement, as defined by a novel
               stochastic model that also considers hardware constraints and intrinsic
               parameters. In addition, as existing solutions to toggle the sensors
               were found to be insufficient, the development of a hardware module,
               especially designed for quick toggling and synchronization with the
               depth stream, is also discussed. The algorithm then is evaluated
               within the scope of a multi-Kinect object tracking scenario and compared
               to other scheduling strategies.},
  pdf       = {IROS12_Faion.pdf}
}

@inproceedings{MFI12_Baum,
  author    = {Marcus Baum and Florian Faion and Uwe D. Hanebeck},
  title     = {{Tracking Ground Moving Extended Objects using RGBD Data}},
  booktitle = {Proceedings of the 2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2012)},
  year      = {2012},
  address   = {Hamburg, Germany},
  month     = sep,
  abstract  = {This paper is about an experimental set-up for tracking a ground moving
               mobile object from a bird's eye view. In this experiment, an RGB
               and depth camera is used for detecting moving points. The detected
               points serve as input for a probabilistic extended object tracking
               algorithm that simultaneously estimates the kinematic parameters
               and the shape parameters of the object. By this means, it is easy
               to discriminate moving objects from the background and the probabilistic
               tracking algorithm ensures a robust and smooth shape estimate. We
               provide an experimental evaluation of a recent Bayesian extended
               object tracking algorithm based on a so-called Random Hypersurface
               Model and give a comparison with active contour models.},
  annote    = {Nominee Best Student Paper Award},
  pdf       = {MFI12_Baum.pdf}
}

@inproceedings{MFI12_Reinhardt,
  author    = {Marc Reinhardt and Benjamin Noack and Uwe D. Hanebeck},
  title     = {{The Hypothesizing Distributed Kalman Filter}},
  booktitle = {Proceedings of the 2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2012)},
  year      = {2012},
  address   = {Hamburg, Germany},
  month     = sep,
  abstract  = {This paper deals with distributed information processing in sensor
               networks. We propose the Hypothesizing Distributed Kalman Filter
               that incorporates an assumption of the global measurement model into
               the distributed estimation process. The procedure is based on the
               Distributed Kalman Filter and inherits its optimality when the assumption
               about the global measurement uncertainty is met. Recursive formulas
               for local processing as well as for fusion are derived. We show that
               the proposed algorithm yields the same results, no matter whether
               the measurements are processed locally or globally, even when the
               process noise is not negligible. For further processing of the estimates,
               a consistent bound for the error covariance matrix is derived. All
               derivations and explanations are illustrated by means of a new classification
               scheme for estimation processes.},
  annote    = {Nominee Best Student Paper Award},
  pdf       = {MFI12_Reinhardt.pdf}
}

@article{SYSAES_Baum,
  author   = {Marcus Baum},
  title    = {{Student Research Highlight: Simultaneous Tracking and Shape Estimation of Extended Targets}},
  journal  = {IEEE Aerospace and Electronic Systems Magazine},
  year     = {2012},
  volume   = {27},
  pages    = {42--44},
  number   = {7},
  month    = jul,
  abstract = {Target tracking algorithms are usually based on the assumption that
              the target extent is small compared to the measurement noise; hence,
              the target is modeled as a mathematical point. However, if the target
              extent is rather large, the target may cause multiple sensor measurements
              from different spatially distributed reflection centers. In this
              case, the modeling of the target extent is essential. In particular,
              the author looks at the random hypersurface model and its role in
              tracking; the tracking method is evaluated using a Microsoft(R) Kinect(TM)
              sensor as an example.},
  doi      = {10.1109/MAES.2012.6328840},
  issn     = {0885-8985},
  url      = {https://dx.doi.org/10.1109/MAES.2012.6328840}
}

@article{TAC12_Deisenroth,
  author   = {Marc Peter Deisenroth and Ryan Darby Turner and Marco F. Huber and Uwe D. Hanebeck and Carl Edward Rasmussen},
  title    = {{Robust Filtering and Smoothing with Gaussian Processes}},
  journal  = {IEEE Transactions on Automatic Control},
  year     = {2012},
  volume   = {57},
  pages    = {1865--1871},
  number   = {7},
  month    = jul,
  abstract = {We propose a principled algorithm for robust Bayesian filtering and
              smoothing in nonlinear stochastic dynamic systems when both the transition
              function and the measurement function are described by non-parametric
              Gaussian process (GP) models. GPs are gaining increasing importance
              in signal processing, machine learning, robotics, and control for
              representing unknown system functions by posterior probability distributions.
              This modern way of system identification is more robust than finding
              point estimates of a parametric function representation. Our principled
              filtering/smoothing approach for GP dynamic systems is based on analytic
              moment matching in the context of the forward-backward algorithm.
              Our numerical evaluations demonstrate the robustness of the proposed
              approach in situations where other state-of-the-art Gaussian filters
              and smoothers can fail.},
  doi      = {10.1109/TAC.2011.2179426},
  issn     = {0018-9286},
  keywords = {GP dynamic systems;analytic moment matching;control systems;forward-backward
              algorithm;machine learning;measurement function;nonlinear stochastic
              dynamic systems;nonparametric Gaussian process;parametric function
              representation;point estimation;posterior probability distributions;robotics;robust
              Bayesian filtering;robust Bayesian smoothing;signal processing;system
              identification;transition function;unknown system function representation;Bayes
              methods;Gaussian processes;identification;nonlinear dynamical systems;nonparametric
              statistics;smoothing methods;statistical distributions;},
  pdf      = {TAC12_Deisenroth.pdf},
  url      = {https://dx.doi.org/10.1109/TAC.2011.2179426}
}

@inproceedings{Fusion12_Baum,
  author    = {Marcus Baum and Florian Faion and Uwe D. Hanebeck},
  title     = {{Modeling the Target Extent with Multiplicative Noise}},
  booktitle = {Proceedings of the 15th International Conference on Information Fusion (Fusion 2012)},
  year      = {2012},
  address   = {Singapore},
  month     = jul,
  abstract  = {Extended target tracking deals with simultaneously tracking the shape
               and the kinematic parameters of a target. In this work, we formulate
               the extended target tracking problem as a state estimation problem
               with both multiplicative and additive measurement noise. In case
               of extended targets with known orientation, we show that the best
               linear estimator is not consistent and, hence, is unsuitable for
               this problem. In order to overcome this issue, we propose a quadratic
               estimator for a recursive closed-form measurement update. Simulations
               demonstrate the performance of the estimator.},
  pdf       = {Fusion12_Baum-MultNoise.pdf}
}

@inproceedings{Fusion12_BaumWillett,
  author    = {Marcus Baum and Peter Willett and Uwe D. Hanebeck},
  title     = {{Calculating Some Exact MMOSPA Estimates for Particle Distributions}},
  booktitle = {Proceedings of the 15th International Conference on Information Fusion (Fusion 2012)},
  year      = {2012},
  address   = {Singapore},
  month     = jul,
  abstract  = {In this work, we present some exact algorithms for calculating the
               minimum mean optimal sub-pattern assignment (MMOSPA) estimate for
               probability densities that are represented with particles. First,
               a polynomial-time algorithm for two targets is derived by reducing
               the problem to the enumeration of the cells of a hyperplane arrangement.
               Second, we present a linear-time algorithm for an arbitrary number
               of scalar target states, which is based on the insight that the MMOSPA
               estimate coincides with the mean of the order statistics.},
  pdf       = {Fusion12_Baum-MMOSPA.pdf}
}

@inproceedings{Fusion12_BenavoliNoack,
  author    = {Alessio Benavoli and Benjamin Noack},
  title     = {{Pushing Kalman's Idea to the Extremes}},
  booktitle = {Proceedings of the 15th International Conference on Information Fusion (Fusion 2012)},
  year      = {2012},
  address   = {Singapore},
  month     = jul,
  abstract  = {The paper focuses on the fundamental idea of Kalman's seminal paper:
               how to solve the filtering problem from the only knowledge of the
               first two moments of the noise terms. In this paper, by exploiting
               set of distributions based filtering, we solve this problem without
               introducing additional assumptions on the distributions of the noise
               terms (e.g., Gaussianity) or on the final form of the estimator (e.g.,
               linear estimator). Given the moments (e.g., mean and variance) of
               random variable X, it is possible to define the set of all distributions
               that are compatible with the moments information. This set of distributions
               can be equivalently characterized by its extreme distributions which
               is a family of mixtures of Dirac's deltas. The lower and upper expectation
               of any function g of X are obtained in correspondence of these extremes
               and can be computed by solving a linear programming problem. The
               filtering problem can then be solved by running iteratively this
               linear programming problem.},
  pdf       = {Fusion12_BenavoliNoack.pdf}
}

@inproceedings{Fusion12_Faion-CylinderTracking,
  author    = {Florian Faion and Marcus Baum and Uwe D. Hanebeck},
  title     = {{Tracking 3D Shapes in Noisy Point Clouds with Random Hypersurface Models}},
  booktitle = {Proceedings of the 15th International Conference on Information Fusion (Fusion 2012)},
  year      = {2012},
  address   = {Singapore},
  month     = jul,
  abstract  = {Depth sensors such as the Microsoft Kinect\texttrademark depth sensor
               provide three dimensional point clouds of an observed scene. In this
               paper, we employ Random Hypersurface Models (RHMs), which is a modeling
               technique for extended object tracking, to point cloud fusion in
               order to track a shape approximation of an underlying object. We
               present a novel variant of RHMs to model shapes in 3D space. Based
               on this novel model, we develop a specialized algorithm to track
               persons by approximating their shapes as cylinders. For evaluation,
               we utilize a Kinect network and simulations based on a stochastic
               sensor model.},
  pdf       = {Fusion12_Faion-CylinderTracking.pdf}
}

@inproceedings{Fusion12_Faion-RecursiveCalibration,
  author    = {Florian Faion and Patrick Ruoff and Antonio Zea and Uwe D. Hanebeck},
  title     = {{Recursive Bayesian Calibration of Depth Sensors with Non-Overlapping Views}},
  booktitle = {Proceedings of the 15th International Conference on Information Fusion (Fusion 2012)},
  year      = {2012},
  address   = {Singapore},
  month     = jul,
  abstract  = {In this paper we present a recursive Bayesian method to calibrate
               rigidly linked depth sensors with non-overlapping fields of view.
               The extrinsic parameters of this setup are obtained by rotating and
               translating both cameras, estimating the local transformations using
               point feature correspondences, and finally using these values to
               recursively find a solution to the matrix equation $\mat{A_k}\mat{X}=\mat{X}\mat{B_k}$.
               The algorithm is based on a Bayesian estimator, which allows the
               consideration of camera-specific measurement noise and permits the
               system to adapt naturally to changes in the extrinsic parameters.
               The derived equations were carefully chosen to be free from singularities.
               This paper also includes a thorough evaluation based on synthetic
               and real data to show the effectiveness of the algorithm.},
  pdf       = {Fusion12_Faion.pdf}
}

@inproceedings{Fusion12_Fischer,
  author    = {J{\"{o}}rg Fischer and Achim Hekler and Uwe D. Hanebeck},
  title     = {{State Estimation in Networked Control Systems}},
  booktitle = {Proceedings of the 15th International Conference on Information Fusion (Fusion 2012)},
  year      = {2012},
  address   = {Singapore},
  month     = jul,
  abstract  = {We consider the problem of state estimation in a Networked Control
               System, where measurements and control inputs are transmitted via
               a communication network. The network is subject to time-varying delays
               and stochastic data losses and does not provide acknowledgments of
               successfully transmitted data packets. A challenge that arises in
               this configuration is that the estimator has only uncertain information
               about the actually applied control inputs. In this paper, we derive
               a multiple-model based estimator that uses the state measurements
               to estimate the applied control inputs so that the overall state
               estimation is improved. The efficiency of the proposed approach is
               demonstrated by means of Monte-Carlo-Simulation runs with an inverted
               pendulum on a cart.},
  pdf       = {Fusion12_Fischer.pdf}
}

@inproceedings{Fusion12_Gilitschenski,
  author    = {Igor Gilitschenski and Uwe D. Hanebeck},
  title     = {{A Robust Computational Test for Overlap of Two Arbitrary-dimensional Ellipsoids in Fault-Detection of Kalman Filters}},
  booktitle = {Proceedings of the 15th International Conference on Information Fusion (Fusion 2012)},
  year      = {2012},
  address   = {Singapore},
  month     = jul,
  abstract  = {On-line fault-detection in uncertain measurement and estimation systems
               is of particular interest in many applications. In certain systems
               based on the Kalman filter, this test can be performed by checking
               whether hyperellipsoids overlap. This test can be applied to detecting
               failure in the system itself or in the sensors used to determine
               the system state. To facilitate the practical application of such
               tests, we describe a simple condition for overlap of two ellipsoids
               and propose an efficient algorithmic implementation for testing this
               condition. There are applications in many other areas, such as collision
               avoidance or computer graphics. Our proposal makes use of Leverriere's
               algorithm and Sturm's theorem, a result of algebraic geometry. Thus,
               no approximative methods, such as root finding or minimization are
               needed. Furthermore, the complexity of the algorithm is fixed for
               a fixed problem dimension.},
  pdf       = {Fusion12_Gilitschenski.pdf}
}

@inproceedings{Fusion12_Hanebeck,
  author    = {Uwe D. Hanebeck and Jannik Steinbring},
  title     = {{Progressive Gaussian Filtering Based on Dirac Mixture Approximations}},
  booktitle = {Proceedings of the 15th International Conference on Information Fusion (Fusion 2012)},
  year      = {2012},
  address   = {Singapore},
  month     = jul,
  abstract  = {In this paper, we propose a progressive Gaussian filter, where the
               measurement information is continuously included into the given prior
               estimate (although we perform observations at discrete time steps).
               The key idea is to derive a system of ordinary first-order differential
               equations (ODE) that is used for continuously tracking the true non-Gaussian
               posterior by its best matching Gaussian approximation. Calculating
               the required moments of the true posterior is performed based on
               corresponding Dirac Mixture approximations. The performance of the
               new filter is evaluated in comparison with state-of-the-art filters
               by means of a canonical benchmark example, the discrete-time cubic
               sensor problem.},
  pdf       = {Fusion12_Hanebeck.pdf}
}

@inproceedings{Fusion12_Hekler,
  author    = {Achim Hekler and J{\"{o}}rg Fischer and Uwe D. Hanebeck},
  title     = {{Control over Unreliable Networks Based on Control Input Densities}},
  booktitle = {Proceedings of the 15th International Conference on Information Fusion (Fusion 2012)},
  year      = {2012},
  address   = {Singapore},
  month     = jul,
  abstract  = {Time delays and data losses arising from an unreliable communication
               between the components of a control loop decrease the quality of
               control and thus, have to be incorporated explicitly in the control
               decision. In this paper, a novel concept, the so-called virtual control
               inputs, is presented, which extends the well-established control
               technique of sending sequences of future inputs by incorporating
               also the potential effects of previously transmitted sequences on
               the future system behavior. The key idea of this paper is to model
               the unknown future inputs as random variables characterized by probability
               density functions over the finite set of potential future inputs.
               Subject to this probabilistic description of the future inputs, the
               controller determines the optimal open-loop sequence over a finite
               horizon. The high capacity of the proposed approach is demonstrated
               by simulations, in which a sensor manager schedules sensors for tracking
               a mobile object.},
  pdf       = {Fusion12_Hekler.pdf}
}

@inproceedings{Fusion12_Noack,
  author    = {Benjamin Noack and Florian Pfaff and Uwe D. Hanebeck},
  title     = {{Combined Stochastic and Set-Membership Information Filtering in Multisensor Systems}},
  booktitle = {Proceedings of the 15th International Conference on Information Fusion (Fusion 2012)},
  year      = {2012},
  address   = {Singapore},
  month     = jul,
  abstract  = {In state estimation theory, stochastic and set-membership approaches
               are generally considered separately from each other. Both concepts
               have distinct advantages and disadvantages making each one inherently
               better suited to model different sources of estimation uncertainty.
               In order to better utilize the potentials of both concepts, the core
               element of this paper is a Kalman filtering scheme that allows for
               a simultaneous treatment of stochastic and set-membership uncertainties.
               An uncertain quantity is herein modeled by a set of Gaussian densities.
               Since many modern applications operate in networked systems that
               may consist of a multitude of local processing units and sensor nodes,
               estimates have to be computed in a distributed manner and measurements
               may arrive at high frequency. An algebraic reformulation of the Kalman
               filter, the information filter, significantly eases the implementation
               of such distributed fusion architectures. This paper explicates how
               stochastic and set-membership uncertainties can simultaneously be
               treated within this information form and compared to the Kalman filter,
               it becomes apparent that the quality of some required approximations
               is enhanced.},
  pdf       = {Fusion12_Noack.pdf}
}

@inproceedings{Fusion12_Packi,
  author    = {Ferdinand Packi and Uwe D. Hanebeck},
  title     = {{Robust NLOS Discrimination for Range-Based Acoustic Pose Tracking}},
  booktitle = {Proceedings of the 15th International Conference on Information Fusion (Fusion 2012)},
  year      = {2012},
  address   = {Singapore},
  month     = jul,
  abstract  = {Indoor localization is a field in research with many competing technologies
               using different kinds of media. A common challenge faced by most
               systems is dealing with Non-Line-of-Sight (NLOS) conditions. We are
               addressing this issue with focus on sound in the frequency range
               above 20 kHz, as we encountered severe occurrence of outliers due
               to multipath propagation, by reflections, and from occlusion. The
               proper discrimination of erroneous signals is of special concern
               during initialization time of the tracking system. During run time,
               the computationally demanding process can be spared, if motion is
               modelled and stochastic filtering techniques are applied. This paper
               depicts solutions for both cases, and demonstrates that a combined
               use of static and dynamic localization methods delivers increased
               robustness at an affordable computational cost.},
  pdf       = {Fusion12_Packi.pdf}
}

@inproceedings{Fusion12_Reinhardt,
  author    = {Marc Reinhardt and Benjamin Noack and Uwe D. Hanebeck},
  title     = {{On Optimal Distributed Kalman Filtering in Non-ideal Situations}},
  booktitle = {Proceedings of the 15th International Conference on Information Fusion (Fusion 2012)},
  year      = {2012},
  address   = {Singapore},
  month     = jul,
  abstract  = {The distributed processing of measurements and the subsequent data
               fusion is called Track-to-Track fusion. Although a solution for the
               Track-to-Track fusion that is equivalent to a central processing
               scheme has been proposed, this algorithm suffers from strict requirements
               regarding the local availability of knowledge about utilized models
               of the remote nodes. By means of simple examples, we investigate
               the effects of incorrectly assumed models and trace the errors back
               to a bias, which we derive in closed form. We propose an extension
               to the exact Track-to-Track fusion algorithm that corrects the bias
               after arbitrarily many time steps. This new approach yields optimal
               results when the assumptions about the measurement models are correct
               and otherwise still provides the exact value for the mean-squared-error
               matrix. The performance of this algorithm is demonstrated and applications
               are presented that, e.g.,~allow the employment of nonlinear filter
               methods.},
  pdf       = {Fusion12_Reinhardt.pdf}
}

@inproceedings{Fusion12_Reinhardt-FastCI,
  author    = {Marc Reinhardt and Benjamin Noack and Uwe D. Hanebeck},
  title     = {{Closed-form Optimization of Covariance Intersection for Low-dimensional Matrices}},
  booktitle = {Proceedings of the 15th International Conference on Information Fusion (Fusion 2012)},
  year      = {2012},
  address   = {Singapore},
  month     = jul,
  abstract  = {The fusion under unknown correlations is an important technique in
               sensor-network information processing as the cross-correlations between
               different estimates remain often unknown to the nodes. Covariance
               intersection is a wide-spread and efficient algorithm to fuse estimates
               under such uncertain conditions. Although different optimization
               criteria have been developed, the trace or determinant minimization
               of the fused covariance matrix seems to be most meaningful. However,
               this minimization requires numeric solutions of a convex optimization
               problem. We derive an algorithm to reduce this nonlinear optimization
               to the well-known polynomial root-finding problem. This allows us
               to present closed-form solutions for the determinant criterion when
               the dimension of the occurring covariance matrices is at most~4 and
               for the trace criterion when the dimension of the covariance matrices
               is at most~3. We demonstrate the effectiveness of the approach by
               means of a speed evaluation.},
  pdf       = {Fusion12_Reinhardt-FastCI.pdf}
}

@inproceedings{ACC12_Hekler,
  author    = {Achim Hekler and Christof Chlebek and Uwe D. Hanebeck},
  title     = {{Open-Loop Feedback Control of Nonlinear Stochastic Systems Based on Deterministic Dirac Mixture Densities}},
  booktitle = {Proceedings of the 2012 American Control Conference (ACC 2012)},
  year      = {2012},
  address   = {Montr\'{e}al, Canada},
  month     = jun,
  abstract  = {The main problem of stochastic nonlinear model predictive control
               (SNMPC) is that the equations for state prediction and calculation
               of the expected reward are in general not solvable in closed form.
               A popular approach is to approximate the occurring continuous probability
               density functions by a discrete density representation, which allows
               an analytical solution of the SNMPC equations. In this paper, we
               propose to draw the samples not randomly as in Monte Carlo based
               methods, but systematically by minimizing a distance measure. In
               doing so, fewer components are generally required to represent the
               underlying probability density while achieving the same approximation
               quality. Especially if the evaluation of the expected reward is computationally
               expensive, this property affects the complexity of computation significantly.
               By means of a path planning problem, we have substantiated this statement
               with several simulation runs.},
  pdf       = {ACC12_Hekler.pdf}
}

@inproceedings{ACC12_Lyons,
  author    = {Daniel Lyons and Jan-Peter Calliess and Uwe D. Hanebeck},
  title     = {{Chance Constrained Model Predictive Control for Multi-Agent Systems with Coupling Constraints}},
  booktitle = {Proceedings of the 2012 American Control Conference (ACC 2012)},
  year      = {2012},
  address   = {Montr\'{e}al, Canada},
  month     = jun,
  abstract  = {We consider stochastic model predictive control of a multi-agent systems
               with constraints on the probabilities of inter-agent collisions.
               First, we propose a method based on sample average approximation
               of the collision probabilities to make the stochastic control problem
               computationally tractable. Its approximation error vanishes for fixed
               control inputs as the number of samples goes to infinity. However,
               empirical results indicate that the complexity of the resulting optimization
               problem can be too high to be solved under under real-time requirements.
               To alleviate the computational burden we propose a second approach
               that uses probabilistic bounds to determine regions of increased
               probability of presence for each agent and introduce constraints
               for the control problem prohibiting overlap of these regions. We
               prove that the resulting problem is conservative for the original
               problem, i.e., every control strategy that is feasible under our
               new constraints will automatically be feasible for the true original
               problem. Furthermore, we present simulations demonstrating better
               run-time performance of our second approach compared to the sample
               average approximation. Finally, we empirically show that our approach
               is better suited than robust control approaches for situations in
               which the systems under control are affected by stochastic disturbances.},
  pdf       = {ACC12_Lyons.pdf}
}

@inproceedings{SPIE12_FischerBaum,
  author    = {Yvonne Fischer and Marcus Baum and Fabian Flohr and Uwe D. Hanebeck and J{\"{u}}rgen Beyerer},
  booktitle = {Signal Processing, Sensor Fusion, and Target Recognition XXI (Proceedings of SPIE)},
  title     = {{Evaluation of Tracking Methods for Maritime Surveillance }},
  year      = {2012},
  address   = {Baltimore, Maryland, USA},
  month     = apr,
  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.},
  doi       = {10.1117/12.919234},
  url       = {https://dx.doi.org/10.1117/12.919234}
}

@inproceedings{TRB12_Kretz,
  author    = {Tobias Kretz and Antonia P{\'{e}}rez Arias and Simon Friedberger and Uwe D. Hanebeck},
  title     = {{Using Extended Range Telepresence to Collect Data of Pedestrian Dynamics}},
  booktitle = {{Proceedings of the Transportation Research Board 91st Annual Meeting (TRB 2012)}},
  year      = {2012},
  address   = {Washington D.C., USA},
  month     = jan,
  abstract  = {In this article a new way to collect data of pedestrian dynamics is
               introduced. A virtual reality system consisting of an extended range
               telepresence system and a microscopic pedestrian simulation is used
               to simplify data collection. The extended range telepresence system
               allows a user to move through a virtual environment by natural walking
               instead of by using conventional input devices, like a joystick.
               The telepresence system is connected to a pedestrian simulation which
               produces real time 3D animated output which is presented to the user
               with a head-mounted display (HMD) capable of showing 3D imagery.
               The simulated pedestrians react to the user of the telepresence as
               if it were another simulated pedestrian. With this system data about
               pedestrian dynamics can be collected in experiments in which not
               all participants need to be real people, but some - ideally all except
               for one - can be simulated. This allows the general collection of
               data about pedestrian dynamics but also to calibrate model specific
               parameters. In this paper three experiments are introduced. However,
               the focus of the contribution is to give an idea and an overview
               of the combined telepresence-simulation system as data collection
               tool.},
  pdf       = {TRB12_Kretz.pdf}
}

@inproceedings{CDC11_Baum,
  author    = {Marcus Baum and Benjamin Noack and Uwe D. Hanebeck},
  title     = {{Random Hypersurface Mixture Models for Tracking Multiple Extended Objects}},
  booktitle = {{Proceedings of the 50th IEEE Conference on Decision and Control (CDC 2011)}},
  year      = {2011},
  address   = {Orlando, Florida, USA},
  month     = dec,
  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.},
  pdf       = {CDC11_Baum.pdf}
}

@inproceedings{CDC11_Hekler,
  author    = {Achim Hekler and Martin Kiefel and Uwe D. Hanebeck},
  title     = {{Stochastic Nonlinear Model Predictive Control with Guaranteed Error Bounds Using Compactly Supported Wavelets}},
  booktitle = {{Proceedings of the 50th IEEE Conference on Decision and Control (CDC 2011)}},
  year      = {2011},
  address   = {Orlando, Florida, USA},
  month     = dec,
  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.},
  pdf       = {CDC11_Hekler.pdf}
}

@inproceedings{ICCV11_Kretz,
  author    = {Tobias Kretz and Stefan Hengst and Vidal Roca and Antonia P{\'{e}}rez Arias and Simon Friedberger and Uwe D. Hanebeck},
  title     = {{Calibrating Dynamic Pedestrian Route Choice with an Extended Range Telepresence System}},
  booktitle = {{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)}},
  year      = {2011},
  address   = {Barcelona, Spain},
  month     = nov,
  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.},
  pdf       = {ICCV11_Kretz.pdf}
}

@inproceedings{Humanoids11_Rybok,
  author    = {Lukas Rybok and Simon Friedberger and Uwe D. Hanebeck and Rainer Stiefelhagen},
  title     = {{The KIT Robo-Kitchen Data set for the Evaluation of View-based Activity Recognition Systems}},
  booktitle = {Proceedings of the 2011 11th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2011)},
  year      = {2011},
  address   = {Bled, Slovenia},
  month     = oct,
  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.},
  pdf       = {Humanoids11_Rybok.pdf}
}

@inproceedings{SDF11_Huber,
  author    = {Marco Huber and Peter Krauthausen and Uwe D. Hanebeck},
  title     = {{Superficial Gaussian Mixture Reduction}},
  booktitle = {{Proceedings of the IEEE ISIF Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF 2011)}},
  year      = {2011},
  address   = {Berlin, Germany},
  month     = oct,
  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.},
  pdf       = {SDF11_Huber.pdf}
}

@inproceedings{IROS11_Baum,
  author    = {Marcus Baum and Uwe D. Hanebeck},
  title     = {{Fitting Conics to Noisy Data Using Stochastic Linearization}},
  booktitle = {Proceedings of the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011)},
  year      = {2011},
  address   = {San Francisco, California, USA},
  month     = sep,
  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.},
  pdf       = {IROS11_Baum.pdf}
}

@inproceedings{IROS11_Gehrig,
  author    = {Dirk Gehrig and Peter Krauthausen and Lukas Rybok and Hildegard K{\"{u}}hne and Tanja Schultz and Uwe D. Hanebeck and Rainer Stiefelhagen},
  title     = {{Combined Intention, Activity, and Motion Recognition for a Humanoid Household Robot}},
  booktitle = {Proceedings of the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011)},
  year      = {2011},
  address   = {San Francisco, California, USA},
  month     = sep,
  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.},
  pdf       = {IROS11_Gehrig.pdf}
}

@inproceedings{IROS11_Perez,
  author    = {Antonia P{\'{e}}rez Arias and Uwe D. Hanebeck},
  title     = {{Motion Control of a Semi-mobile Haptic Interface for Extended Range Telepresence}},
  booktitle = {Proceedings of the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011)},
  year      = {2011},
  address   = {San Francisco, California, USA},
  month     = sep,
  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.},
  pdf       = {IROS11_Perez.pdf}
}

@inproceedings{TGF11_Kretz,
  author    = {Tobias Kretz and Stefan Hengst and Antonia P{\'{e}}rez Arias and Simon Friedberger and Uwe D. Hanebeck},
  title     = {{Using Extended Range Telepresence to Investigate Route Choice Behavior}},
  booktitle = {{Proceedings of the Traffic and Granular Flow Conference 2011 (TGF 2011)}},
  year      = {2011},
  address   = {Moscow, Russia},
  month     = sep,
  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.},
  pdf       = {TGF11_Kretz.pdf}
}

@inproceedings{IFAC11_Huber,
  author    = {Marco F. Huber and Frederik Beutler and Uwe D. Hanebeck},
  booktitle = {Proceedings of the 18th IFAC World Congress (IFAC 2011)},
  title     = {{(Semi-)Analytic Gaussian Mixture Filter}},
  year      = {2011},
  address   = {Milan, Italy},
  month     = aug,
  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.},
  pdf       = {IFAC11_Huber.pdf}
}

@inproceedings{IFAC11_Noack,
  author    = {Benjamin Noack and Marcus Baum and Uwe D. Hanebeck},
  title     = {{Automatic Exploitation of Independencies for Covariance Bounding in Fully Decentralized Estimation}},
  booktitle = {Proceedings of the 18th IFAC World Congress (IFAC 2011)},
  year      = {2011},
  address   = {Milan, Italy},
  month     = aug,
  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.},
  pdf       = {IFAC11_Noack.pdf}
}

@inproceedings{Fusion11_Baum-RHM,
  author    = {Marcus Baum and Uwe D. Hanebeck},
  title     = {{Shape Tracking of Extended Objects and Group Targets with Star-Convex RHMs}},
  booktitle = {Proceedings of the 14th International Conference on Information Fusion (Fusion 2011)},
  year      = {2011},
  address   = {Chicago, Illinois, USA},
  month     = jul,
  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.},
  annote    = {Winner Best Student Paper Award Certificate (PDF)},
  pdf       = {Fusion11_Baum-RHM.pdf}
}

@inproceedings{Fusion11_Baum-USF,
  author    = {Marcus Baum and Uwe D. Hanebeck},
  title     = {{Using Symmetric State Transformations for Multi-Target Tracking}},
  booktitle = {Proceedings of the 14th International Conference on Information Fusion (Fusion 2011)},
  year      = {2011},
  address   = {Chicago, Illinois, USA},
  month     = jul,
  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.},
  pdf       = {Fusion11_Baum-USF.pdf}
}

@inproceedings{Fusion11_Baum,
  author    = {Marcus Baum and Benjamin Noack and Frederik Beutler and Dominik Itte and Uwe D. Hanebeck},
  title     = {{Optimal Gaussian Filtering for Polynomial Systems Applied to Association-free Multi-Target Tracking}},
  booktitle = {Proceedings of the 14th International Conference on Information Fusion (Fusion 2011)},
  year      = {2011},
  address   = {Chicago, Illinois, USA},
  month     = jul,
  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.},
  pdf       = {Fusion11_Baum.pdf}
}

@inproceedings{Fusion11_Bogatyrenko,
  author    = {Evgeniya Bogatyrenko and Uwe D. Hanebeck},
  title     = {{Adaptive Model-Based Visual Stabilization of Image Sequences Using Feedback}},
  booktitle = {Proceedings of the 14th International Conference on Information Fusion (Fusion 2011)},
  year      = {2011},
  address   = {Chicago, Illinois, USA},
  month     = jul,
  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.},
  pdf       = {Fusion11_Bogatyrenko.pdf}
}

@inproceedings{Fusion11_Krauthausen,
  author    = {Peter Krauthausen and Patrick Ruoff and Uwe D. Hanebeck},
  title     = {{Sparse Mixture Conditional Density Estimation by Superficial Regularization}},
  booktitle = {Proceedings of the 14th International Conference on Information Fusion (Fusion 2011)},
  year      = {2011},
  address   = {Chicago, Illinois, USA},
  month     = jul,
  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.},
  pdf       = {Fusion11_Krauthausen.pdf}
}

@inproceedings{Fusion11_Noack,
  author    = {Benjamin Noack and Marcus Baum and Uwe D. Hanebeck},
  title     = {{Covariance Intersection in Nonlinear Estimation Based on Pseudo Gaussian Densities}},
  booktitle = {Proceedings of the 14th International Conference on Information Fusion (Fusion 2011)},
  year      = {2011},
  address   = {Chicago, Illinois, USA},
  month     = jul,
  abstract  = {Many modern fusion architectures are designed to process and fuse
               data in networked systems. Alongside the advantages, such as scalability and
               robustness, distributed fusion techniques particularly have to tackle the problem of dependencies
               between locally processed data. In linear estimation problems, uncertain
               quantities with unknown cross-correlations can be fused by means
               of the covariance intersection algorithm, which avoids overconfident
               fusion results. However, for nonlinear system dynamics and sensor
               models perturbed by arbitrary noise, it is not only a problem to
               characterize and parameterize dependencies between estimates, but
               also to find a proper notion of consistency. This paper addresses
               these issues by transforming the state estimates to a different state
               space, where the corresponding densities are Gaussian and only linear
               dependencies between estimates, i.e., correlations, can arise. These
               pseudo Gaussian densities then allow the notion of covariance consistency
               to be used in distributed nonlinear state estimation.},
  pdf       = {Fusion11_Noack.pdf}
}

@inproceedings{Fusion11_Reinhardt,
  author    = {Marc Reinhardt and Benjamin Noack and Marcus Baum and Uwe D. Hanebeck},
  title     = {{Analysis of Set-theoretic and Stochastic Models for Fusion under Unknown Correlations}},
  booktitle = {Proceedings of the 14th International Conference on Information Fusion (Fusion 2011)},
  year      = {2011},
  address   = {Chicago, Illinois, USA},
  month     = jul,
  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.},
  pdf       = {Fusion11_Reinhardt.pdf}
}

@inproceedings{Fusion11_Ruoff,
  author    = {Patrick Ruoff and Peter Krauthausen and Uwe D. Hanebeck},
  title     = {{Progressive Correction for Deterministic Dirac Mixture Approximations}},
  booktitle = {Proceedings of the 14th International Conference on Information Fusion (Fusion 2011)},
  year      = {2011},
  address   = {Chicago, Illinois, USA},
  month     = jul,
  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.},
  pdf       = {Fusion11_Ruoff.pdf}
}

@inproceedings{ACC11_Bogatyrenko,
  author    = {Evgeniya Bogatyrenko and Uwe D. Hanebeck},
  title     = {{Visual Stabilization of Beating Heart Motion by Model-based Transformation of Image Sequences}},
  booktitle = {Proceedings of the 2011 American Control Conference (ACC 2011)},
  year      = {2011},
  address   = {San Francisco, California, USA},
  month     = jun,
  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.},
  pdf       = {ACC11_Bogatyrenko.pdf}
}

@inproceedings{ACC11_Huber,
  author    = {Marco F. Huber and Frederik Beutler and Uwe D. Hanebeck},
  title     = {{Semi-Analytic Gaussian Assumed Density Filter}},
  booktitle = {Proceedings of the 2011 American Control Conference (ACC 2011)},
  year      = {2011},
  address   = {San Francisco, California, USA},
  month     = jun,
  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.},
  pdf       = {ACC11_Huber.pdf}
}

@inproceedings{ACC11_Krauthausen,
  author    = {Peter Krauthausen and Masoud Roschani and Uwe D. Hanebeck},
  title     = {{Incorporating Prior Knowledge into Nonparametric Conditional Density Estimation}},
  booktitle = {Proceedings of the 2011 American Control Conference (ACC 2011)},
  year      = {2011},
  address   = {San Francisco, California, USA},
  month     = jun,
  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.},
  pdf       = {ACC11_Krauthausen.pdf}
}

@inproceedings{ACC11_Noack,
  author    = {Benjamin Noack and Daniel Lyons and Matthias Nagel and Uwe D. Hanebeck},
  title     = {{Nonlinear Information Filtering for Distributed Multisensor Data Fusion}},
  booktitle = {Proceedings of the 2011 American Control Conference (ACC 2011)},
  year      = {2011},
  address   = {San Francisco, California, USA},
  month     = jun,
  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.},
  pdf       = {ACC11_Noack.pdf}
}

@inproceedings{WHC11_Perez,
  author    = {Antonia P{\'{e}}rez Arias and Henning P. Eberhardt and Florian Pfaff and Uwe D. Hanebeck},
  title     = {{T}he {P}lenhaptic {G}uidance {F}unction for {I}ntuitive {N}avigation in {E}xtended {R}ange {T}elepresence {S}cenarios},
  booktitle = {Proceedings of the IEEE World Haptics Conference (WHC 2011)},
  year      = {2011},
  address   = {Istanbul, Turkey},
  month     = jun,
  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.},
  pdf       = {WHC11_Perez.pdf}
}

@inproceedings{ARMS_AAMAS11_Lyons,
  author    = {Jan-P. Calliess and Daniel Lyons and Uwe D. Hanebeck},
  title     = {{Lazy auctions for multi-robot collision avoidance and motion control under uncertainty}},
  booktitle = {Autonomous Robots and Multirobot Systems (ARMS) Workshop at AAMAS 2011},
  year      = {2011},
  address   = {Taipei, Taiwan},
  month     = may,
  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.},
  pdf       = {ARMS11_CalliessLyons.pdf}
}

@inproceedings{EWSN11_Schmid,
  author    = {Johannes Schmid and Frederik Beutler and Benjamin Noack and Uwe D. Hanebeck and Klaus D. M{\"{u}}ller-Glaser},
  title     = {{An Experimental Evaluation of Position Estimation Methods for Person Localization in Wireless Sensor Networks}},
  booktitle = {Proceedings of the 8th European Conference on Wireless Sensor Networks (EWSN 2011)},
  publisher = {Springer},
  editor    = {Pedro Jos\'{e} Marr{\'{o}}n and Kamin Whitehouse},
  pages     = {147--162},
  volume    = {6567},
  year      = {2011},
  address   = {Bonn, Germany},
  month     = feb,
  abstract  = {In this paper, the localization of persons by means of a Wireless
               Sensor Network (WSN) is considered. Persons carry on-body sensor
               nodes and move within a WSN. The location of each person is calculated
               on this node and communicated through the network to a central data
               sink for visualization. Applications of such a system could be found in
               mass casualty events, firefighter scenarios, hospitals or retirement homes for example.
               For the location estimation on the sensor node, three derivatives of the
               Kalman Filter and a closed-form solution (CFS) are applied, compared,
               and evaluated in a real-world scenario. A prototype 65-node ZigBee WSN
               is implemented and data are collected in in- and outdoor environments
               with differently positioned on-body nodes. The described estimators are
               then evaluated off-line on the experimentally collected data.
               The goal of this paper is to present a comprehensive real-world evaluation of methods for
               person localization in a WSN based on received signal strength (RSS) range measurements.
               It is concluded that person localization in in- and outdoor environments is possible
               under the considered conditions with the considered filters. The compared methods
               allow for suffciently accurate localization results and are robust against
               inaccurate range measurements.},
  doi       = {10.1007/978-3-642-19186-2_10},
  url       = {https://dx.doi.org/10.1007/978-3-642-19186-2_10},
  pdf       = {EWSN11_Schmid.pdf}
}

@inproceedings{CDC10_Hekler,
  author    = {Achim Hekler and Martin Kiefel and Uwe D. Hanebeck},
  title     = {{Nonlinear Bayesian Estimation with Compactly Supported Wavelets}},
  booktitle = {Proceedings of the 2010 IEEE Conference on Decision and Control (CDC 2010)},
  year      = {2010},
  address   = {Atlanta, Georgia, USA},
  month     = dec,
  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.},
  pdf       = {CDC10_HeklerKiefel.pdf}
}

@article{TM10_Noack,
  author   = {Benjamin Noack and Vesa Klumpp and Daniel Lyons and Uwe D. Hanebeck},
  title    = {{M}odellierung von {U}nsicherheiten und {Z}ustandssch{\"{a}}tzung mit {M}engen von {W}ahrscheinlichkeitsdichten},
  journal  = {tm -- Technisches Messen, Oldenbourg Verlag},
  year     = {2010},
  volume   = {77},
  pages    = {544--550},
  number   = {10},
  month    = oct,
  abstract = {Die systematische Behandlung von Unsicherheiten stellt eine wesentliche
              Herausforderung in der Informationsfusion dar. Einerseits m{\"{u}}ssen
              geeignete Darstellungsformen f{\"{u}}r die Unsicherheiten bestimmt
              werden und andererseits darauf aufbauend effiziente Sch{\"{a}}tzverfahren
              hergeleitet werden. Im Allgemeinen wird zwischen stochastischen und
              mengenbasierten Unsicherheitsbeschreibungen unterschieden. Dieser
              Beitrag stellt ein Verfahren zur Zustandssch{\"{a}}tzung vor, welches
              simultan stochastische und mengenbasierte Fehlergr{\"o}{\ss}en ber{\"u}cksichtigen
              kann, indem unsichere Gr{\"{o}}{\ss}en nicht mehr durch eine einzelne
              Wahrscheinlichkeitsdichte, sondern durch eine Menge von Dichten repr{\"{a}}sentiert
              werden. Besonderes Augenmerk liegt hier auf den Vorteilen und Anwendungsm{\"{o}}glichkeiten
              dieser Unsicherheitsbeschreibung.},
  doi      = {10.1524/teme.2010.0087},
  editor   = {Klaus-Dieter Sommer and Fernando {Puente Le{\'{o}}n} and Michael Heizmann},
  pdf      = {TM10_Noack.pdf},
  url      = {https://dx.doi.org/10.1524/teme.2010.0087}
}

@article{TM10_Kuwertz,
  author   = {Achim Kuwertz and Marco F. Huber and Felix Sawo and Uwe D. Hanebeck},
  title    = {{Modellbasierte Quellenverfolgung in r{\"{a}}umlich ausgedehnten Ph{\"{a}}nomenen mittels Sensoreinsatzplanung}},
  journal  = {tm -- Technisches Messen, Oldenbourg Verlag},
  year     = {2010},
  volume   = {77},
  pages    = {551--557},
  number   = {10},
  month    = oct,
  abstract = {Bewegte Quellen k{\"{o}}nnen durch Emission r{\"{a}}umlich
              ausgedehnte Ph{\"{a}}nomene 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 Ph{\"{a}}nomens zur Verf{\"{u}}gung - etwa unter Verwendung
              station{\"{a}}rer oder mobiler Sensoren. Dieser Beitrag stellt
              modellbasierte Verfahren f{\"{u}}r eine echtzeitf{\"{a}}hige Lokalisierung
              und Verfolgung von bewegten Quellen vor. Zur gezielten Maximierung
              des Informationsgehalts der Messungen wird dabei eine vorausschauende
              Sensoreinsatzplanung genutzt, welche eine hohe Lokalisierungsg{\"{u}}te bei
              geringem Aufwand erm{\"{o}}glicht.},
  doi      = {10.1524/teme.2010.0088},
  editor   = {Klaus-Dieter Sommer and Fernando {Puente Le{\'o}n} and Michael Heizmann},
  pdf      = {TM10_Kuwertz.pdf},
  url      = {https://dx.doi.org/10.1524/teme.2010.0088}
}

@inproceedings{IROS10_Beutler,
  author    = {Frederik Beutler and Marco F. Huber and Uwe D. Hanebeck},
  title     = {{Optimal Stochastic Linearization for Range-based Localization}},
  booktitle = {Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010)},
  year      = {2010},
  address   = {Taipei, Taiwan},
  month     = oct,
  pdf       = {IROS10_Beutler.pdf},
  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.}
}

@inproceedings{IROS10_Bogatyrenko,
  author    = {Evgeniya Bogatyrenko and Benjamin Noack and Uwe D. Hanebeck},
  title     = {{Reliable Estimation of Heart Surface Motion under Stochastic and Unknown but Bounded Systematic Uncertainties}},
  booktitle = {Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 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.},
  year      = {2010},
  address   = {Taipei, Taiwan},
  month     = oct,
  pdf       = {IROS10_BogatyrenkoNoack.pdf}
}

@inproceedings{IROS10_Packi,
  author    = {Ferdinand Packi and Antonia P\'{e}rez Arias and Frederik Beutler and Uwe D. Hanebeck},
  title     = {{A Wearable System for the Wireless Experience of Extended Range Telepresence}},
  booktitle = {Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010)},
  year      = {2010},
  address   = {Taipei, Taiwan},
  month     = oct,
  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.},
  pdf       = {IROS10_PackiBeutler.pdf}
}

@inproceedings{IROS10_Perez,
  author    = {Antonia P{\'{e}}rez Arias and Uwe D. Hanebeck},
  title     = {{Wide-Area Haptic Guidance: Taking the User by the Hand}},
  booktitle = {Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010)},
  year      = {2010},
  address   = {Taipei, Taiwan},
  month     = oct,
  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.},
  pdf       = {IROS10_Perez.pdf}
}

@inproceedings{IROS10_Krauthausen,
  author    = {Peter Krauthausen and Uwe D. Hanebeck},
  title     = {{A Model-Predictive Switching Approach To Efficient Intention Recognition}},
  booktitle = {Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010)},
  year      = {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.},
  address   = {Taipei, Taiwan},
  month     = oct,
  pdf       = {IROS10_Krauthausen.pdf}
}

@inproceedings{SDF10_Baum,
  author    = {Marcus Baum and Michael Feldmann and Dietrich Fr{\"{a}}nken and Uwe D. Hanebeck and Wolfgang Koch},
  booktitle = {{Proceedings of the IEEE ISIF Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF 2010)}},
  title     = {{Extended Object and Group Tracking: A Comparison of Random Matrices and Random Hypersurface Models}},
  year      = {2010},
  address   = {Leipzig, Germany},
  month     = oct,
  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.},
  pdf       = {SDF10_BaumFeldmann.pdf}
}

@article{IJCARS10_Bogatyrenko,
  author    = {Evgeniya Bogatyrenko and Pascal Pompey and Uwe D. Hanebeck},
  title     = {{Efficient Physics-Based Tracking of Heart Surface Motion for Beating Heart Surgery Robotic Systems}},
  journal   = {International Journal of Computer Assisted Radiology and Surgery (IJCARS 2010)},
  year      = {2010},
  month     = aug,
  pages     = {387--399},
  doi       = {doi:10.1007/s11548-010-0517-5},
  volume    = {6},
  number    = {3},
  publisher = {Springer Berlin / Heidelberg},
  issn      = {1861-6410},
  pdf       = {IJCARS10_Bogatyrenko.pdf},
  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.},
  url       = {https://dx.doi.org/10.1007/s11548-010-0517-5},
  note      = {doi:10.1007/s11548-010-0517-5}
}

@book{KI10_Hanebeck,
  title     = {KI 2010: Advances in Artificial Intelligence, Proceedings of the 33rd Annual German Conference on AI, Karlsruhe, Germany},
  publisher = {Springer},
  year      = {2010},
  editor    = {R{\"{u}}diger Dillmann and J{\"{u}}rgen Beyerer and Uwe D. Hanebeck and Tanja Schultz},
  series    = {Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence},
  month     = sep
}

@inproceedings{KI10_Krauthausen,
  author    = {Peter Krauthausen and Uwe D. Hanebeck},
  title     = {{Situation-Specific Intention Recognition for Human-Robot-Cooperation}},
  booktitle = {{33rd Annual German Conference on Artificial Intelligence (KI 2010)}},
  publisher = {Gesellschaft f{\"u}r Informatik e.V.},
  year      = {2010},
  address   = {Karlsruhe, Germany},
  month     = sep,
  pdf       = {KI10_Krauthausen.pdf},
  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.}
}

@inproceedings{IPIN10_Packi,
  author    = {Ferdinand Packi and Frederik Beutler and Uwe D. Hanebeck},
  title     = {{Wireless Acoustic Tracking for Extended Range Telepresence}},
  booktitle = {Proceedings of the 2010 IEEE International Conference on Indoor Positioning and Indoor Navigation (IPIN 2010)},
  year      = {2010},
  address   = {Z{\"u}rich, Switzerland},
  month     = sep,
  pdf       = {IPIN10_Packi.pdf},
  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.}
}

@inproceedings{VECIMS10_Baum,
  author    = {Ioana Gheta and Marcus Baum and Andrey Belkin and J{\"{u}}rgen Beyerer and Uwe D. Hanebeck},
  booktitle = {{Proceedings of the 2010 IEEE International Conference on Virtual Environments, Human-Computer Interfaces and Measurement Systems (VECIMS 2010)}},
  title     = {{Three Pillar Information Management System for Modeling the Environment of Autonomous Systems}},
  year      = {2010},
  address   = {Taranto, Italy},
  month     = sep,
  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.},
  pdf       = {VECIMS10_GhetaBaum.pdf}
}

@inproceedings{ASE10_Baum,
  author    = {Reiner H\"{a}hnle and Marcus Baum and Richard Bubel and Marcel Rothe},
  title     = {{A Visual Interactive Debugger Based on Symbolic Execution}},
  booktitle = {Proceedings of the 25th IEEE/ACM International Conference on Automated Software Engineering (ASE 2010)},
  year      = {2010},
  address   = {Antwerp, Belgium},
  month     = sep
}

@inproceedings{MFI10_Baum,
  author    = {Marcus Baum and Uwe D. Hanebeck},
  title     = {{Association-free Tracking of Two Closely Spaced Targets}},
  booktitle = {Proceedings of the 2010 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2010)},
  year      = {2010},
  address   = {Salt Lake City, Utah, USA},
  month     = sep,
  pdf       = {MFI10_Baum.pdf},
  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.}
}

@inproceedings{MFI10_BaumRect,
  author    = {Marcus Baum and Uwe D. Hanebeck},
  title     = {{Tracking a Minimum Bounding Rectangle based on Extreme Value Theory}},
  booktitle = {Proceedings of the 2010 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2010)},
  year      = {2010},
  address   = {Salt Lake City, Utah, USA},
  month     = sep,
  pdf       = {MFI10_BaumRect.pdf},
  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.}
}

@inproceedings{MFI10_BaumGheta,
  author    = {Marcus Baum and Ioana Gheta and Andrey Belkin and J{\"u}rgen Beyerer and Uwe D. Hanebeck},
  title     = {{Data Association in a World Model for Autonomous Systems}},
  booktitle = {Proceedings of the 2010 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2010)},
  year      = {2010},
  address   = {Salt Lake City, Utah, USA},
  month     = sep,
  pdf       = {MFI10_BaumGheta.pdf},
  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.}
}

@inproceedings{MFI10_Beutler,
  author    = {Frederik Beutler and Marco F. Huber and Uwe D. Hanebeck},
  title     = {{Semi-Analytic Stochastic Linearization for Range-Based Pose Tracking}},
  booktitle = {Proceedings of the 2010 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2010)},
  year      = {2010},
  address   = {Salt Lake City, Utah, USA},
  month     = sep,
  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.},
  pdf       = {MFI10_Beutler.pdf}
}

@inproceedings{MFI10_Bogatyrenko,
  author    = {Evgeniya Bogatyrenko and Uwe D. Hanebeck},
  title     = {{Simultaneous State and Parameter Estimation for Physics-Based Tracking of Heart Surface Motion}},
  booktitle = {Proceedings of the 2010 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2010)},
  year      = {2010},
  address   = {Salt Lake City, Utah, USA},
  month     = sep,
  pdf       = {MFI10_Bogatyrenko.pdf},
  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.}
}

@inproceedings{MFI10_KrauthausenEberhardt,
  author    = {Peter Krauthausen and Henning Eberhardt and Uwe D. Hanebeck},
  title     = {{Multivariate Parametric Density Estimation Based On The Modified Cram\'{e}r-von Mises Distance}},
  booktitle = {Proceedings of the 2010 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2010)},
  year      = {2010},
  address   = {Salt Lake City, Utah, USA},
  month     = sep,
  abstract  = {In this paper, a novel distance-based density
               estimation method is proposed, which considers the overall
               density function in the goodness-of-fit. In detail, the parameters
               of Gaussian mixture densities are estimated from samples,
               based on the distance of the cumulative distributions over
               the entire state space. Due to the ambiguous definition of the
               standard multivariate cumulative distribution, the Localized
               Cumulative Distribution and a modified Cram\'{e}r-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.},
  pdf       = {MFI10_KrauthausenEberhardt.pdf}
}

@inproceedings{MFI10_Krauthausen,
  author    = {Peter Krauthausen and Uwe D. Hanebeck},
  title     = {{Regularized Non-Parametric Multivariate Density and Conditional Density Estimation}},
  booktitle = {Proceedings of the 2010 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2010)},
  year      = {2010},
  address   = {Salt Lake City, Utah, USA},
  month     = sep,
  abstract  = {In this paper, a distance-based method for both
               multivariate non-parametric density and conditional density
               estimation is proposed. The contributions are the formulation
               of both density estimation problems as weight optimization
               problems for Gaussian mixtures centered about samples with
               identical parameters. Furthermore, the minimization is based
               on the modified Cram\'{e}r-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.},
  pdf       = {MFI10_Krauthausen.pdf}
}

@inproceedings{MFI10_LyonsHekler,
  author    = {Daniel Lyons and Achim Hekler and Markus Kuderer and Uwe D. Hanebeck},
  booktitle = {Proceedings of the 2010 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2010)},
  title     = {{Robust Model Predictive Control with Least Favorable Measurements}},
  year      = {2010},
  address   = {Salt Lake City, Utah, USA},
  month     = sep,
  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.},
  annote    = {Nominee Best Paper Award},
  pdf       = {MFI10_LyonsHekler.pdf}
}

@inproceedings{MSC10_HeklerLyonsNoack,
  author    = {Achim Hekler and Daniel Lyons and Benjamin Noack and Uwe D. Hanebeck},
  title     = {{Nonlinear Model Predictive Control Considering Stochastic and Systematic Uncertainties with Sets of Densities}},
  booktitle = {Proceedings of the IEEE Multi-Conference on Systems and Control (MSC 2010)},
  address   = {Yokohama, Japan},
  year      = {2010},
  month     = sep,
  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.},
  pdf       = {MSC10_HeklerLyonsNoack.pdf}
}

@inproceedings{Fusion10_BaumKlumpp,
  author    = {Marcus Baum and Vesa Klumpp and Uwe D. Hanebeck},
  booktitle = {Proceedings of the 13th International Conference on Information Fusion (Fusion 2010)},
  title     = {{A Novel Bayesian Method for Fitting a Circle to Noisy Points}},
  year      = {2010},
  address   = {Edinburgh, United Kingdom},
  month     = jul,
  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.},
  pdf       = {Fusion10_BaumKlumpp.pdf}
}

@inproceedings{Fusion10_BaumNoack,
  author    = {Marcus Baum and Benjamin Noack and Uwe D. Hanebeck},
  booktitle = {Proceedings of the 13th International Conference on Information Fusion (Fusion 2010)},
  title     = {{Extended Object and Group Tracking with Elliptic Random Hypersurface Models}},
  year      = {2010},
  address   = {Edinburgh, United Kingdom},
  month     = jul,
  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.},
  pdf       = {Fusion10_BaumNoack.pdf}
}

@inproceedings{Fusion10_Beutler,
  author    = {Frederik Beutler and Uwe D. Hanebeck},
  title     = {{A Two-Step Approach for Offset and Position Estimation from Pseudo-Ranges Applied to Multilateration Tracking}},
  booktitle = {Proceedings of the 13th International Conference on Information Fusion (Fusion 2010)},
  address   = {Edinburgh, United Kingdom},
  year      = {2010},
  pdf       = {Fusion10_Beutler.pdf},
  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.},
  month     = jul
}

@inproceedings{Fusion10_DunauPacki,
  author    = {Patrick Dunau and Ferdinand Packi and Frederik Beutler and Uwe D. Hanebeck},
  title     = {{Efficient Multilateration Tracking with Concurrent Offset Estimation using Stochastic Filtering Techniques}},
  booktitle = {Proceedings of the 13th International Conference on Information Fusion (Fusion 2010)},
  address   = {Edinburgh, United Kingdom},
  year      = {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-

repository: KIT-ISAS/ISAS-Publications

repo_name: KIT-ISAS/ISAS-Publications