A next-best-view algorithm for autonomous 3D object modeling by a humanoid robot

We present our investigation to make humanoids build autonomously geometric models of unknown objects. Although good methods have been proposed for the specific problem of the next-best-view during the modeling and the recognition process; our approach is different and takes into account humanoid specificities in terms of embedded vision sensor and redundant motion capabilities. The problem to select the best next view of interest at each modeling step is formulated as an optimization problem where the whole robot posture needs to be defined jointly with the robot cameraspsila position and orientation. To achieve this, we propose a differentiable formula that expresses the amount of unknown data visible from a specific viewpoint, given only knowledge acquired in previous steps. In addition, a specific stability constraint is introduced to allow the robot to reach a configuration where its feet can be moved away from their initial position.