Rondao, DuarteAouf, NabilRichardson, Mark A.Dubois-Matra, Olivier2020-04-142020-04-142020-04-07Rondao 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. 100-1220094-5765https://doi.org/10.1016/j.actaastro.2020.03.049http://dspace.lib.cranfield.ac.uk/handle/1826/15392Optical-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.enAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/BenchmarkingFeature detectorsFeature descriptorsMultispectral imagingSpace relative navigationArticle26647199