Object part segmentation and classification in range images for grasping

Recognition by Components (RBC) has been one of the most conceptually significant frameworks for modeling human visual object recognition. Extension of the model to practical robotic applications have been traditionally limited by the lack of good response in textureless areas in the case of conventional inexpensive stereo cameras as well as by the need for expensive laser based sensor systems to compensate for this deficiency. The recent availability of RGB-D sensors such as the PrimeSense sensor has opened new avenues for practical usage of these sensors for robotic applications such as grasping. In this paper, we present novel algorithms for segmentation of objects and parts from range images with extensions based on semantic cues to yield robust part detection. The detected parts are then parameterized using a superquadric based fitting framework and classified into one of different generic shapes. The categorization of the parts enables rules for grasping the object. This Grasping by Components (GBC) scheme is a natural extension of the RBC framework and provides a scalable framework for grasping of objects. This scheme also permits the grasping of novel objects in the scene, with at least one known grasp affordance.