Uwe D. Hanebeck

WLAN-Based Pedestrian Tracking Using Particle Filters and Low-Cost MEMS Sensors

Indoor positioning systems based on wireless LAN (WLAN) are being widely investigated in academia and industry. Meanwhile, the emerging low-cost MEMS sensors can also be used as another independent positioning source. In this paper, we propose a pedestrian tracking framework based on particle filters, which extends the typical WLAN-based indoor positioning systems by integrating low-cost MEMS accelerometer and map information. Our simulation and real world experiments indicate a remarkable performance improvement by using this fusion framework.

On entropy approximation for Gaussian mixture random vectors

For many practical probability density representations such as for the widely used Gaussian mixture densities, an analytic evaluation of the differential entropy is not possible and thus, approximate calculations are inevitable. For this purpose, the first contribution of this paper deals with a novel entropy approximation method for Gaussian mixture random vectors, which is based on a component-wise Taylor-series expansion of the logarithm of a Gaussian mixture and on a splitting method of Gaussian mixture components. The employed order of the Taylor-series expansion and the number of components used for splitting allows balancing between accuracy and computational demand. The second contribution is the determination of meaningful and efficiently to calculate lower and upper bounds of the entropy, which can be also used for approximation purposes. In addition, a refinement method for the more important upper bound is proposed in order to approach the true entropy value.

Random Hypersurface Models for extended object tracking

Target tracking algorithms usually assume that the received measurements stem from a point source. However, in many scenarios this assumption is not feasible so that measurements may stem from different locations, named measurement sources, on the target surface. Then, it is necessary to incorporate the target extent into the estimation procedure in order to obtain robust and precise estimation results. This paper introduces the novel concept of Random Hypersurface Models for extended targets. A Random Hypersurface Model assumes that each measurement source is an element of a randomly generated hypersurface. The applicability of this approach is demonstrated by means of an elliptic target shape. In this case, a Random Hypersurface Model specifies the random (relative) Mahalanobis distance of a measurement source to the center of the target object. As a consequence, good estimation results can be obtained even if the true target shape significantly differs from the modeled shape. Additionally, Random Hypersurface Models are computationally tractable with standard nonlinear stochastic state estimators.

Extended object and group tracking with Elliptic Random Hypersurface Models

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.

Localization of a mobile robot using relative bearing measurements

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

Analytic moment-based Gaussian process filtering

We propose an analytic moment-based filter for nonlinear stochastic dynamic systems modeled by Gaussian processes. Exact expressions for the expected value and the covariance matrix are provided for both the prediction step and the filter step, where an additional Gaussian assumption is exploited in the latter case. Our filter does not require further approximations. In particular, it avoids finite-sample approximations. We compare the filter to a variety of Gaussian filters, that is, the EKF, the UKF, and the recent GP-UKF proposed by Ko et al. (2007).

ROMAN: a mobile robotic assistant for indoor service applications

The paper describes design issues of a mobile service robot for health care applications and domestic automation. Key components required for achieving semi-autonomous operation are surveyed, including: 1. a highly maneuverable locomotion platform, 2. an anthropomorphous manipulator, 3. a reliable multisensor system, and a 4. multimodal human-robot-interface. In addition, related information processing and control methodologies are presented. Special emphasis is put on a system architecture for integration of the individual components into a full-size service robot. Performance and usefulness of the proposed approaches are demonstrated through experiments in various real-world service scenarios.

Gaussian Filter based on Deterministic Sampling for High Quality Nonlinear Estimation

In this paper, a Gaussian filter for nonlinear Bayesian estimation is introduced that is based on a deterministic sample selection scheme. For an effective sample selection, a parametric density function representation of the sample points is employed, which allows approximating the cumulative distribution function of the prior Gaussian density. The computationally demanding parts of the optimization problem formulated for approximation are carried out off-line for obtaining an efficient filter, whose estimation quality can be altered by adjusting the number of used sample points. The improved performance of the proposed Gaussian filter compared to the well-known unscented Kalman filter is demonstrated by means of two examples.

A novel approach to proactive human-robot cooperation

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

Extended Object Tracking with Random Hypersurface Models

The random hypersurface model (RHM) is introduced for estimating a shape approximation of an extended object in addition to its kinematic state. An RHM represents the spatial extent by means of randomly scaled versions of the shape boundary. In doing so, the shape parameters and the measurements are related via a measurement equation that serves as the basis for a Gaussian state estimator. Specific estimators are derived for elliptic and star-convex shapes.