Robust feature-free pose tracking and uncertainty-aware geometry reconstruction for spinning non-cooperative targets

Abstract

Pose tracking and geometry reconstruction are greatly significant for the high-level perceptual understanding and close-proximity operations of dynamic and geometrically unknown non-cooperative targets in space. However, the performance degrades severely under the commonly outlier-contaminated and corrupted measurements acquired from Time-of-Flight (ToF) cameras. In this paper, we are motivated to investigate the framework of both robust pose tracking and reliable geometry reconstruction. For the pose tracking, we propose an improved robust Iterative Closest Point (ICP) method based on adaptive Iteratively Reweighted Least Squares (IRLS), which can gradually jump out of the local minima in a naturally coarse-to-fine fashion. Besides, we put forward a hybrid feature-free loop closure detection approach to efficiently eliminate the accumulated error, meanwhile avoiding the ambiguity caused by the symmetrical structure. Regrading the geometry reconstruction, we present an explicit and general geometry uncertainty description, incorporated into a mixture-based probabilistic fusion method, to cope with the reconstruction defects. The experimental results show that our pose tracking and geometry reconstruction methods can achieve better performance in terms of robustness and accuracy.