Fig. 10. Robot Executing the Task Programmed in the Vrr Environment Fig. 8. Vrr Environment 3.1. Scene Segmentation Fig. 3. Range Image

Virtual reality robotics (VRR) needs sensing feedback from the real environment. To show how advanced 3D vision provides new perspectives to fulfill these needs, this paper presents an architecture and system that integrates hybrid 3D vision and VRR and reports about experiments and results. The first section discusses the advantages of virtual reality in robotics, the potential of a 3D vision system in VRR and the contribution of a knowledge database, robust control and the combination of intensity and range imaging to build such a system. Section two presents the different modules of a hybrid 3D vision architecture based on hypothesis generation and verification. Section three addresses the problem of the recognition of complex, free-form 3D objects and shows how and why the newer approaches based on geometric matching solve the problem. This free-form matching can be efficiently integrated in a VRR system as a hypothesis generation knowledge-based 3D vision system. In the fourth part, we introduce the hypothesis verification based on intensity images which checks object pose and texture. Finally, we show how this system has been implemented and operates in a practical VRR environment used for an assembly task.