A neighbor surface variation variance (NSVV) based key points selection method for the pose estimation of unstructured terrain

Abstract Proximity navigation of asteroid exploration missions requires high accurate point cloud registration for pose estimation of unstructured terrain. To enhance the registration accuracy, a neighbor surface variation variance (NSVV) based key points selection method is proposed to accurately extract key points and improve the pose estimation accuracy of the unstructured terrain. The proposed method extracts key points by top-percentage NSVV ranking in ascending order, which generates high-quality matching pairs with high data removal percentage and reduces the registration time-consuming. The matching pairs are further used for the coarse and fine registration to obtain the optimal pose estimation. The simulation experiment results show that the rotation error and translation error with 90% data removal percentage are 0.692° and 0.0513 m, which can be reduced by 59.44% and 45.48%, respectively. The proposed method offers an efficient, accurate and practical solution for unstructured terrain target pose estimation, paving the way for further advancements in proximity navigation of asteroid exploration missions.