Noncooperative Spacecraft Pose Estimation Based on Point Cloud and Optical Image Feature Collaboration

Pose estimation plays a crucial role in on-orbit servicing technologies. Currently, point cloud registration-based pose estimation methods for noncooperative spacecraft still face the issue of misalignment due to similar point cloud structural features. This paper proposes a pose estimation approach for noncooperative spacecraft based on the point cloud and optical image feature collaboration, inspired by methods such as Oriented FAST and Rotated BRIEF (ORB) and Robust Point Matching (RPM). The method integrates ORB feature descriptors with point cloud feature descriptors, aiming to reduce point cloud mismatches under the guidance of a transformer mechanism, thereby improving pose estimation accuracy. We conducted simulation experiments using the constructed dataset. Comparison with existing methods shows that the proposed approach improves pose estimation accuracy, achieving a rotation error of 0.84° and a translation error of 0.022 m on the validation set. Robustness analysis reveals the method’s stability boundaries within a 30-frame interval. Ablation studies validate the effectiveness of both ORB features and the transformer layer. Finally, we established a ground test platform, and the experimental data results validated the proposed method’s practical value.