Abstract: Aiming at pose estimation for non-cooperative targets in on-orbit maintenance operations of space robots, we propose a pose estimation algorithm based on point cloud registration. Firstly, the hybrid filtering algorithm preserves the shape of the point cloud to the greatest extent while reducing its density. Then, the principal component analysis algorithm is used to establish the eigenvector transformation. The RANSAC algorithm is employed for coarse registration, followed by the improved ICP algorithm for fine registration, which results in the estimation of the rotation matrix, translation matrix, and attitude. Experiments are carried out to evaluate the performance of the algorithm. Simulated satellite point cloud models processed by rotation-translation transformations and Gaussian noise are used to verify the point cloud registration performance. The satellite model scene collected by a TOF camera point cloud is used to validate the pose estimation algorithm. The results show that the proposed algorithm has improved anti-noise performance, and showed higher robustness compared to traditional registration algorithms, with rotation attitude angle error less than 0.4° and displacement error less than 3 mm.

Key words: spatial non-cooperative targets, point cloud registration, pose estimation, TOF camera