Stereo-based 6D object localization for grasping with humanoid robot systems

Robust vision-based grasping is still a hard problem for humanoid robot systems. When being restricted to using the camera system built-in into the robot’s head for object localization, the scenarios get often very simplified in order to allow the robot to grasp autonomously. Within the computer vision community, many object recognition and localization systems exist, but in general, they are not tailored to the application on a humanoid robot. In particular, accurate 6D object localization in the camera coordinate system with respect to a 3D rigid model is crucial for a general framework for grasping. While many approaches try to avoid the use of stereo calibration, we will present a system that makes explicit use of the stereo camera system in order to achieve maximum depth accuracy. Our system can deal with textured objects as well as objects that can be segmented globally and are defined by their shape. Thus, it covers the cases of objects with complex texture and complex shape. Our work is directly linked to a grasping framework being implemented on the humanoid robot ARMAR and serves as its perception module for various grasping and manipulation experiments in a kitchen scenario.