Fast 6D Object Pose Estimation of Shell Parts for Robotic Assembly

Abstract

Shell parts which have similar and close inner and outer surfaces are common in industrial manufacturing applications. In view of the 6D pose error compensation of parts in high-precision robotic assembly tasks, this work proposes a fast 6D pose estimation approach tailored for shell parts. With a binocular structured light camera, the proposed approach consists of two phases, namely initial pose estimation phase and local pose estimation phase. In the former one, an initial pose correction and translation offset methods serve to solve the local optimal estimation problem of the iterative closest point (ICP) algorithm. This problem is caused by the poorly assigned initial pose and the similar inner and outer surfaces of shell parts. In the latter one, the voxel sampling and the weighted point-to-plane ICP algorithms are applied to boost the efficiency of the pose estimation approach. With two typical shell parts, a simulation and an experiment of pose estimation are conducted to verify the effectiveness of the proposed approach. Experiment results prove that the accuracy of the pose estimation approach is 0:27mm/0:38°, and the runtime is 680ms.