Michael Fiegert

Multisensor Based Indoor Vehicle Localization System for Production and Logistic

In this paper a multisensor based indoor vehicle localization system for production and logistics is introduced. To track the position and the orientation of a moving vehicle a set of distance values to several points on the vehicle is measured by a wireless ranging system. The beacons of the wireless system are mounted at known positions in the surrounding infrastructure. It is shown that conventional multilateralization is not very practical to solve the required positioning task. In order to match the complete geometry of the forklift to a set of measured distance data, a heuristic nonlinear optimization method is applied. With our novel approach it is possible to solve the complex underlying transformation problem and to calculate the position and angle of the forklift for nearly arbitrary measuring conditions. The achieved accuracy is optimal in the least squares sense. For situations where the wireless access to the vehicle is disturbed, the localization system is assisted by data from a laser scanner. By matching subsequent scans relative movements of the vehicle are be determined precisely. The fusion of an optical relative sensors and a wireless absolute localization system allows for a flexible and steady control of transportation processes even in complex and dynamically changing environments

Probabilistic Rule Set Joint State Update as approximation to the full joint state estimation applied to multi object scene analysis

One essential capability of service robots lies in the identification and localization of objects in the vicinity of the robot. The extreme computational demands of this high-dimensional state estimation problem require approximations of the joint posterior even for small numbers of objects. A common approach to solve this problem is to marginalize the joint state space and to consider object-related state spaces which are estimated individually under the assumption of statistical independence. In practice, however, this independence assumption is often violated, especially when the objects are located close to each other, which leads to a reduced accuracy of this approximation, compared to the full joint estimation. To address this problem, we propose the new method denoted as Rule Set Joint State Update (RSJSU), which features a better approximation of the joint posterior in the presence of dependencies, and thus leads to better estimation results. We present experimental results in which we simultaneously estimate all six degrees of freedom of multiple objects.

Scene Analysis for Service Robots

A scene analysis module for service robots is presented which uses SIFT in a stereo setting, a systematic handling of uncertainties and an active perception component. The system is integrated and evaluated on the DESIRE two-arm mobile robot. Complex everyday scenes composed of various items from a 100-object database are analyzed successfully and efficiently.

Handling Environment Dynamics in Medial Axis Based Localization

For mobile robot localization we use a map based on the medial axis of free space. It combines the generality of occupancy grids with the efficiency of geometric feature maps. In contrast to these, no global consistent coordinate frame is needed and no special features like lines or corner points need to be present in the environment. Therefore the approach is very universal with respect to the size and type of environment.

A new concept for a cooperative fusion platform

The increasing traffic and the increasing number of sensors both in cars and in the infrastructure pose new challenges but also create new opportunities for traffic control. If the sensor data in various states of interpretation and aggregation could be shared and reused, it would be possible to minimize accidents and improve the traffic situation. In this paper we describe an approach to automatically configure sensor data fusion systems across the boundaries of independent subsystems, where information on all levels can be exchanged. The basis for this is a formal description of all required meta-information that enables the reasoning for automatic configuration.

Rule Set Based Joint State Update

The accurate localization of the objects in the environment is one of the fundamental preconditions for the reliability of service robots. The majority of algorithms for object localization lacks the ability to integrate physical commonsense knowledge into the recognition process especially, when multiple objects are envolved. Consequently the estimates of such methods often do not comply with basic physical constraints such as that rigid objects should not intersect. In this paper, we present an approach for multi-object localization that is able to consider such physical constraints as statistical dependencies in state estimation processes to increase the localization accuracy. Extensive experiments carried out with a real robot in the context of a service robotics scenario demonstrate the practical usefulness of our approach.