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Benchmarking of local feature detectors and descriptors for multispectral relative navigation in space

Rondao, D. ORCID: 0000-0001-9438-0267, Aouf, N. ORCID: 0000-0001-9291-4077, Richardson, M. A. & Dubois-Matra, O. (2020). Benchmarking of local feature detectors and descriptors for multispectral relative navigation in space. Acta Astronautica, 172, pp. 100-122. doi: 10.1016/j.actaastro.2020.03.049


Optical-based navigation for space is a field growing in popularity due to the appeal of efficient techniques such as Visual Simultaneous Localisation and Mapping (VSLAM), which rely on automatic feature tracking with low-cost hardware. However, low-level image processing algorithms have traditionally been measured and tested for ground-based exploration scenarios. This paper aims to fill the gap in the literature by analysing state-of-the-art local feature detectors and descriptors with a taylor-made synthetic dataset emulating a Non-Cooperative Rendezvous (NCRV) with a complex spacecraft, featuring variations in illumination, rotation, and scale. Furthermore, the performance of the algorithms on the Long Wavelength Infrared (LWIR) is investigated as a possible solution to the challenges inherent to on-orbit imaging in the visible, such as diffuse light scattering and eclipse conditions. The Harris, GFTT, DoG, Fast-Hessian, FAST, CenSurE detectors and the SIFT, SURF, LIOP, ORB, BRISK, FREAK descriptors are benchmarked for images of Envisat. It was found that a combination of Fast-Hessian with BRISK was the most robust, while still capable of running on a low resolution and acquisition rate setup. For large baselines, the rate of false-positives increases, limiting their use in model-based strategies.

Publication Type: Article
Additional Information: © 2023. This manuscript version is made available under the CC-BY-NC-ND 4.0 license in new tab/window)
Publisher Keywords: Benchmarking, Feature detectors, Feature descriptors, Multispectral imaging, Space relative navigation
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TL Motor vehicles. Aeronautics. Astronautics
Departments: School of Science & Technology > Engineering
SWORD Depositor:
[thumbnail of Benchmarking_of_local_feature_detectors-2020.pdf]
Text - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

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