High-speed multi-dimensional relative navigation for uncooperative space objects

Abstract

This work proposes a high-speed Light Detection and Ranging (LIDAR) based navigation architecture that is appropriate for uncooperative relative space navigation applications. In contrast to current solutions that exploit 3D LIDAR data, our architecture transforms the odometry problem from the 3D space into multiple 2.5D ones and completes the odometry problem by utilizing a recursive filtering scheme. Trials evaluate several current state-of-theart 2D keypoint detection and local feature description methods as well as recursive filtering techniques on a number of simulated but credible scenarios that involve a satellite model developed by Thales Alenia Space (France). Most appealing performance is attained by the 2D keypoint detector Good Features to Track (GFFT) combined with the feature descriptor KAZE, that are further combined with either the H∞ or the Kalman recursive filter. Experimental results demonstrate that compared to current algorithms, the GFTT/ KAZE combination is highly appealing affording one order of magnitude more accurate odometry and a very low processing burden, which depending on the competitor method, may exceed one order of magnitude faster computation.

Robust On-Manifold Optimization for Uncooperative Space Relative Navigation with a Single Camera

“…This is often overcome by resorting to more precise active sensors, such as Lidar, which have the advantage of supplying range information and being invariant to illumination changes [3,4]. In addition to asteroids, Lidar has also been successfully used for relative navigation with artificial satellites [5] and developments in the field continue being made [6], but it remains challenging to integrate in on-board systems due to size and power constraints.…”

Three-dimensional shape reconstruction of uncooperative spacecraft with texture-guided depth upsampling

“…However, the high sensitivity to the extreme illumination conditions in space makes the use of passive cameras as the main shape measurement sensor extremely challenging [5]. As regards the latter aspect, active EO sensors such as LIDARs and ToF cameras are more robust when operating under harsh illumination conditions [17][18][19] and provide direct depth measurements of the observed object [20][21][22], making them a viable alternative for measuring the target surface [20]. In space missions, LIDARs have been widely applied to six degrees of freedom pose estimation [23][24][25], whereas ToF cameras are employed to retrieve characteristics of the spacecraft, such as the actual size and surface geometry [26].…”

DeepLO: Multi-projection deep LIDAR odometry for space orbital robotics rendezvous relative navigation

“…Therefore, the Source spacecraft must estimate its relative position and attitude with respect to the Target platform by utilizing only its onboard sensors. Current solutions involve 2D visual data in a monocular [1,2] or a stereo camera configuration [3][4][5][6], 2D Infrared (IR) thermal data [7], and 3D Light Detection and Ranging (LIDAR) data [8,9,[18][19][20][21][22][23][24][10][11][12][13][14][15][16][17]. A comprehensive review of spacecraft pose determination techniques for close-proximity operations is presented in [25].…”

“…This work is further extended in [35] where several combinations of 3D features and recursive filtering schemes are evaluated on both real and simulated scenarios. Kechagias-Stamatis et al [24] suggest a non-standard space navigation solution where 3D…”