Position Awareness Network for Noncooperative Spacecraft Pose Estimation Based on Point Cloud

Abstract

Spacecraft pose estimation plays a vital role in many on-orbit space missions, such as rendezvous and docking, debris removal, and on-orbit maintenance. At present, the mainstream descriptors-based pose estimation methods ignore the fact that satellite point cloud contains many similar structures, generating numerous mismatched correspondence pairs, and leading to low pose estimation accuracy. This article proposes a position awareness network (PANet) for spacecraft pose estimation. Specifically, the point cloud is first fed into a hierarchical embedding network to extract the key points and construct local structural descriptors. We also build the relative position features by encoding the relative position between key points and reference points. The matching matrix between point clouds is then calculated by comprehensively considering the local structure descriptors and relative location features. In this way, the problem of ambiguous matching caused by similar local structures is avoided. Finally, weighted singular value decomposition (SVD) is utilized to solve the pose between the point clouds based on the correspondence pairs generated by the matching matrix. Besides, a large-scale satellite point cloud dataset is also constructed for training and testing pose estimation algorithms. Empirical experiments on the dataset demonstrate the effectiveness of the proposed PANet, which achieves 1.18$^\circ$ rotation error and 0.136 m translate error, surpassing state-of-the-art methods by a large margin.