INS/LiDAR Relative Navigation Design Based on Point Cloud Covariance Characteristics for Spacecraft Proximity Operation

This paper proposes a pose estimation algorithm using INS and LiDAR for precise cooperative relative navigation between target and chaser spacecraft in a close docking mission scenario. Previous cooperative algorithms have proposed estimating position and pose transformations using typical matching methods or to pre-extract and utilize features from point cloud data. However, in the case of general proximity rendezvous docking, a straight-line approach scenario with very few changes in attitude is usually assumed, and, in this case, pose estimation using simple matching techniques or feature point extraction leads to inaccurate results. To solve this problem, this paper performed a principal component analysis (PCA) based on ICP to align the initial transformation matrix. To keep the distribution of point cloud data constant, the point cloud at the time of docking was applied to ICP to minimize the change in the distribution of point clouds over time. Finally, we designed an EKF filter that estimates the relative position, velocity, and attitude using the INS model and combines it with the relative pose estimated from the point cloud; the proposed method showed the results of estimating the relative pose more effectively than the previous method. The simulation and experiment showed more accurate estimation results than the ICP method in position and attitude, respectively. In particular, in the case of position, both the simulation and experiment showed 0.46 m and 0.32 m better estimation results in the z-axis. Also, attitude estimation showed 0.11° and 2.66° better results in roll and 0.01° and 0.34° better results in pitch. This shows that the proposed algorithm provided better pose estimation results in the docking scenario in a straight line.