Towards autonomous object reconstruction for visual search by the humanoid robot HRP-2
This paper deals with the problem of object reconstruction for visual search by a humanoid robot. Three problems necessary to achieve the behavior autonomously are considered: full-body motion generation according to a camera pose, general object representation for visual recognition and pose estimation, and far-away visual detection of an object. First we deal with the problem of generating full body motion for a HRP-2 humanoid robot to achieve camera pose given by a Next Best View algorithm. We use an optimization based approach including self-collision avoidance. This is made possible by a body to body distance function having a continuous gradient. The second problem has received a lot of attention for several decades, and we present a solution based on 3D vision together with SIFTs descriptor, making use of the information available from the robot. It is shown in this paper that one of the major limitation of this model is the perception distance. Thus a new approach based on a generative object model is presented to cope with more difficult situations. It relies on a local representation which allows handling occlusion as well as large scale and pose variations.