Pose Estimation for Spacecraft Relative Navigation Using Model-Based Algorithms

This paper presents the performance assessment of innovative model-based algorithms developed for pose estimation of uncooperative targets by processing sparse three-dimensional point clouds. This topic is of interest in the framework of advanced space applications, e.g., on-orbit servicing and active debris removal, which require a chaser spacecraft to execute autonomous relative navigation maneuvers in close-proximity of a space target. Both the problems of pose acquisition and tracking are addressed. The former one is carried out by combining the concepts of principal component analysis and template matching in order to limit both the computational effort and the amount of on-board data storage compared with traditional approaches. The latter is entrusted to a customized implementation of the iterative closest point algorithm, which adopts multiple model-measurement matching strategies as well as a refinement step, in order to respectively increase robustness and accelerate algorithm convergence. Also, safe transition from acquisition to tracking is implemented by means of autonomous detection of failures of the pose acquisition algorithm. The performance of the proposed techniques is investigated by means of numerical simulations in which the operation of an active LIDAR system as well as the target-chaser relative dynamics are realistically reproduced. Results demonstrate algorithms' effectiveness over a wide range of relative pose conditions and dealing with targets of variable size and shape, in spite of considerable sparseness of the measured datasets.