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

Kechagias-Stamatis, O., Aouf, N. ORCID: 0000-0001-9291-4077 & Richardson, M. A. (2019). High-speed multi-dimensional relative navigation for uncooperative space objects. Acta Astronautica, 160, pp. 388-400. doi: 10.1016/j.actaastro.2019.04.050


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-the-art 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.

Publication Type: Article
Additional Information: © Elsevier 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license
Publisher Keywords: Multi-dimensional processing; Relative navigation; Spaceborne LIDAR; Uncooperative target
Subjects: T Technology > TL Motor vehicles. Aeronautics. Astronautics
U Military Science
Departments: School of Science & Technology > Engineering > Mechanical Engineering & Aeronautics
Text - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

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