Evaluating 3D local descriptors and recursive filtering schemes for LIDAR‐based uncooperative relative space navigation

We propose a light detection and ranging (LIDAR)-based relative navigation scheme that is appropriate for uncooperative relative space navigation applications. Our technique combines the encoding power of the three-dimensional (3D) local descriptors that are matched exploiting a correspondence grouping scheme, with the robust rigid transformation estimation capability of the proposed adaptive recursive filtering techniques. Trials evaluate several current state-of-the-art 3D local descriptors and recursive filtering techniques on a number of both real and simulated scenarios that involve various space objects including satellites and asteroids. Results demonstrate that the proposed architecture affords a 50% odometry accuracy improvement over current solutions, while also affording a low computational burden. From our trials we conclude that the 3D descriptor histogram of distances short (HoD-S) combined with the adaptive alpha beta filtering poses the most appealing combination for the majority of the scenarios evaluated, as it combines high quality odometry with a low processing burden.