Object categorization and grasping by parts from range scan data

Object category recognition and localization in 3D range data is of great importance in robot manipulation. In this work we propose a novel approach for object categorization and grasping that is focused on topological shape segmentation. The method allows generation of watertight triangulated models of the objects and their shape segmentation into parts. This segmentation provides meaningful information about grasp affordances. An efficient technique for encoding proximity data from range scans is also presented as well as an advanced strategy for manipulation of object sub-parts. Experiments are reported in a real environment using a robot arm equipped with eye-in-hand laser scanner and a parallel gripper.