This paper focuses on two aspects of a human robot interaction scenario: Detection and tracking of moving objects, e.g., persons is necessary for localizing possible interaction partners and reconstruction of the surroundings can be used for navigation purposes and room categorization. Although these processes can be addressed independent from each other, we show that using the available data in exchange enables a more exact reconstruction of the static scene. A 6D data representation consisting of 3D Time-ofFlight (ToF) Sensor data and computed 3D velocities allows segmenting the scene into clusters with consistent velocities. A weak object model is applied to localize and track objects within a particle filter framework. As a consequence, points emerging from moving objects can be neglected during reconstruction. Experiments demonstrate enhanced reconstruction results in comparison to pure bottom-up methods, especially for very short image sequences.
In this paper we present a new system for a mobile robot to generate an articulated scene model by analyzing complex dynamic 3D scenes. The system extracts essential knowledge about the foreground, like moving persons, and the background, which consists of all visible static scene parts. In contrast to other 3D reconstruction approaches, we suggest to additionally distinguish between static parts, like walls, and movable objects like chairs or doors. The discrimination supports the reconstruction process and additionally, delivers important information about interaction objects. Here, the movable object detection is realized object independent by analyzing changes in the scenery. Furthermore, in the proposed system the background scene is feedbacked to the tracking part yielding a much better tracking and detection result which improves again the 3D reconstruction. We show in our experiments that we are able to provide a sound background model and to extract simultaneously persons and object regions representing chairs, doors, and even smaller movable objects.
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