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Evaluating 3D local descriptors and recursive filtering schemes for LIDAR‐based uncooperative relative space navigation
Author(s) -
KechagiasStamatis Odysseas,
Aouf Nabil,
Dubanchet Vincent
Publication year - 2020
Publication title -
journal of field robotics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.152
H-Index - 96
eISSN - 1556-4967
pISSN - 1556-4959
DOI - 10.1002/rob.21904
Subject(s) - odometry , histogram , computer science , artificial intelligence , computer vision , transformation (genetics) , ranging , lidar , pattern recognition (psychology) , remote sensing , geography , image (mathematics) , mobile robot , robot , telecommunications , biochemistry , chemistry , gene
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 αβ filtering poses the most appealing combination for the majority of the scenarios evaluated, as it combines high quality odometry with a low processing burden.