Semantic Component Association within Object Classes Based on Convex Polyhedrons

Abstract

Most objects are composed of semantically distinctive parts that are more or less geometrically distinctive as well. Points on the object relevant for a certain robot operation are usually determined by various physical properties of the object, such as its dimensions or weight distribution, and by the purpose of object parts. A robot operation defined for a particular part of a representative object can be transferred and adapted to other instances of the same object class by detecting the corresponding components. In this paper, a method for semantic association of the object’s components within the object class is proposed. It is suitable for real-time robotic tasks and requires only a few previously annotated representative models. The proposed approach is based on the component association graph and a novel descriptor that describes the geometrical arrangement of the components. The method is experimentally evaluated on a challenging benchmark dataset.