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-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.

    Highlights Multi-dimensional architecture that combines the advantages of the 2D and 3D data space. High-speed odometry architecture with processing burden of milliseconds. One order of magnitude more accurate compared to current solutions. Evaluation of current 2D local feature methods and recursive filtering techniques.


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    Title :

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


    Contributors:

    Published in:

    Acta Astronautica ; 160 ; 388-400


    Publication date :

    2019-04-27


    Size :

    13 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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