Real-time CAD model matching for mobile manipulation and grasping

Humanoid robotic assistants need capable and comprehensive perception systems that enable them to perform complex manipulation and grasping tasks. This requires the identification and recognition of supporting planes and objects in the world, together with their precise 6D poses. In this paper, we propose a 3D perception system architecture that can robustly fit CAD models in cluttered table setting scenes for the purpose of grasping with a mobile manipulator. Our approach uses a powerful combination of two different camera technologies, time-of-flight (TOF) and RGB, to robustly segment the scene and extract object clusters. Using an a-priori database of object models we then perform a CAD matching in 2D camera images. We validate the proposed system in a number of experiments, and compare the system's performance and reliability with similar initiatives.