Pose determination using point features

Intelligent robots equipped with sophisticated sensors and online reasoning capabilities are expected to play major roles in future space operations. Such robots use their sensors to perceive their surroundings, reason about what action plan to do next, and execute the action plan. We extended the capability of our initial system and successfully demonstrated the system in the control of a PUMA 762 robot equipped with a wrist-mounted CCD camera and a wrist-mounted force sensor in the placing of an orbital replacement unit (ORU) onto its base at Goddard Space Flight Center. We employed a simple vision subsystem in both systems. The vision subsystem proceeds as follows: (1) digitizes images, (2) extracts compact homogeneous regions, (3) matches these regions against the stored object model, and (4) computes the pose of the object. While developing these systems, we found that the performance of the vision subsystem is critical to the overall success of the placement of the ORU task. Two of the most important performance issues demanding further analyses are the robustness of the vision subsystem under various lighting conditions and the accuracy of the computed 3-D pose of objects. In this paper, we report on the analyses of the vision subsystem. We found that a simple adaptive global thresholding method works quite well for images taken under various lighting conditions. We also found that the pose computed using the quadrangle method is reasonable for real images. The accuracy of the computed pose can be significantly reduced when image feature noise is present. However, we found that such a problem can be solved by using a 3-D marker and an alternative pose computation algorithm. Simulation results are included.© (1992) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.