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

Date published

2020-04-07

Free to read from

2021-04-08

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Journal Title

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Volume Title

Publisher

Elsevier

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Type

Article

ISSN

0094-5765

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Citation

Rondao D, Aouf N, Richardson MA, Dubois-Matra O. (2020) Benchmarking of local feature detectors and descriptors for multispectral relative navigation in space, Acta Astronautica, Volume 172, July 2020, pp. 100-122

Abstract

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.

Description

Software Description

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Github

Keywords

Benchmarking, Feature detectors, Feature descriptors, Multispectral imaging, Space relative navigation

DOI

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Attribution-NonCommercial-NoDerivatives 4.0 International

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