Robust shape recovery for sparse contact location and normal data from haptic exploration

3D shape reconstruction of objects from tactile exploration data acquired by a multi-fingered robot hand is an important skill for a humanoid robot system. Tactile exploration data captured using current robot technology is naturally sparse and noisy, therefore a satisfying shape estimate is difficult to achieve. In this paper we describe a robust approach for 3D shape recovery using superquadric functions, which makes use of both contact location and normal information. We present two quality measures and compare to other relevant estimation techniques using representative synthetic contact data.