Optimization‐Based Geometric Enhancement and Motion Estimation for Non‐Cooperative Spacecrafts

ABSTRACT The state estimation of non‐cooperative spacecrafts is a crucial prerequisite for on‐orbit services. Aiming at the challenges in the fusion‐based scheme with monocular vision and sparse point cloud, an optimization‐based method of geometric enhancement and motion estimation is proposed in this paper. First, with the novel idea of geometric shape representation using simple features, a real‐time segmentation framework is established. Differing from segmentation models, it can guarantee both complete segmentation and high inference speed. Second, given the assumption of local shared planes, a new label‐free algorithm of point cloud densification is developed with an explainable model. To improve its efficiency, a curvature‐guided strategy is employed to sample depth‐incomplete points conducive to feature enhancement. Compared with sparse point clouds, it shows higher pose observation accuracy. Third, a truncation compensator is built to fit the high‐order terms of a nonlinear state transition model with online optimization, which mitigates the impairment in a priori estimation. Combined with the adaptive extended Kalman filter, the motion can be estimated with fewer errors. Finally, the proposed method is validated through comparative simulations and ground experiments.