Fast 6D object pose estimation of shell parts for robotic assembly

Shell parts which have symmetric structure, even surface and flat color are common in industrial manufacturing applications. Moreover, the inner and outer surface of them are of similar shape and close to each other. These features make the pose estimation process of the shell parts challenging. Aiming at 6D pose error compensation of parts in high-precision robotic assembly tasks, this work proposes a fast 6D pose estimation method tailored for the shell parts. With a binocular structured light camera to acquire the point cloud data, the proposed method consists of two phases, namely the initial pose estimation phase and local pose estimation phase. In the former phase, principle component analysis algorithm is utilized to calculate a primary pose estimation. An initial pose correction and translation offset methods serve to solve the local optimal estimation problem of the iterative closest point (ICP) algorithm. In the latter phase, the voxel sampling and a novel weighted point-to-plane ICP algorithms are applied to boost the efficiency of the pose estimation method. With two typical shell parts, a simulation and an experiment of pose estimation are conducted to validate the effectiveness of the proposed method. Experiment results prove that both the accuracy and efficiency of the pose estimation method meet the requirement of the given assembly tasks.