A Visual Servoing Approach for a Six Degrees-of-Freedom Industrial Robot by RGB-D Sensing

Abstract

A visual servoing approach is presented that uses depth images for robot-pose estimation utilizing a marker- less solution. By matching a predefined robot model to a captured depth image for each robot link, utilizing an appro- priate approximation method like the Iterative Closest Point (ICP) algorithm, the robot´s joint pose can be estimated. The a-priori knowledge of the robot configuration, alignment, and its environment enables a joint pose manipulation by a visual servoed system with potential to collision detection and avoidance. By the use of two RGB-D cameras a more accurate matching of the robot´s links is feasible while avoiding occlusions. The modeled links are coupled as a kinematic chain by the Denavit-Hartenberg convention, and are prevented from divergence during the matching phase by the implementation of an algorithm for joint pose dependency. The required joint orientation of the robot is calculated by the ICP algorithm to perform a pose correction until its point cloud align with the model again. First tests with two structured light cameras indicated that the recognition of the robot´s joint positions brings good results but currently only for slow motion tasks.