Describes a vision system for obstacle detection in mobile robot navigation. The system uses an image processing board equipped with an MPEG motion estimation processor that calculates a robust optic-flow-like vector field in real-time. This field is then evaluated by algorithms running in software on the host PC. As the solutions to the general problem of structure and motion from optic flow are too instable for the use in this application, the typical constraints of mobile robotics are exploited, i.e. a reduced set of motion parameters and a known ground plane. Ego-motion can then be reconstructed with robust one dimensional methods. A new criterion for obstacles that copes well with the noise properties of the motion field is introduced. For vectors belonging to obstacles the 3D information is reconstructed allowing not only qualitative detection of obstacles but quantitative path planning.

Predictive displays have proven their suitability to compensate time delays in visual feedback which impair the performance of teleoperation applications. Most current implementations employ simple computer graphics for the prediction of the visual feedback given to the operator who is controlling the remote manipulation. Furthermore, they require a well-known remote environment. The aim of the presented work is to improve the efficacy of the predictive display by increasing the degree of immersion and by enhancing the system's flexibility. Potential applications include maintenance and repair of machines located in remote plant- or office-type environments, which are typically non-static and a priori only partially known. Communication media are high- and medium-bandwidth LANs and WANs inducing round trip delays varying from 0.2 to 2 seconds. Additionally, a sufficient bandwidth cannot be assured during the entire remote operation. The paper describes a predictive display system which achieves photo-realism based on autonomously explored and updated scene models. Model acquisition is performed with a binocular CCD camera system. Photo-realism is obtained by using computer graphics techniques, especially the mapping of original, real textures. Besides the concepts, an early stage of implementation and first experimental results in scene reconstruction and photo-realistic scene prediction are presented.

Describes a PC-based vision system that can be used to detect moving objects from a mobile robot. An image processing board equipped with an MPEG motion estimation processor calculates a sparse but robust optic flow in real-time. An algorithm to evaluate this kind of optic flow has been realized in software. It determines relevant motion parameters and a simple scene interpretation in terms of moving object regions. The image processing board and the algorithm are presented in some detail; the performance of the system is demonstrated by experiments.

The design of mobile robots which can cope with unexpected disturbances like obstacles or misplaced objects is an active field of research. Such an autonomous robot assesses the situation by comparing data from one or more sensors with an internal representation of its environment. In this paper we present a hierarchically structured world model that combines a general geometric object representation with sensor- and task-specific features and therefore can be used for various sensors and perception tasks. By predicting only those features that, first, can be detected by the sensor and, secondly, are relevant for the current perception task, sensor data interpretation gets faster and more robust. This is illustrated at an exemplary perception task concerning the video-based determination of the joint states of articulated objects.