Browsing by Author "Dubois-Matra, Olivier"

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    Benchmarking of local feature detectors and descriptors for multispectral relative navigation in space
    (Elsevier, 2020-04-07) Rondao, Duarte; Aouf, Nabil; Richardson, Mark A.; Dubois-Matra, Olivier
    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.
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    Multispectral image processing for navigation using low performance computing
    (International Astronautical Federation (IAF), 2018-10) Rondao, Duarte; Aouf, Nabil; Dubois-Matra, Olivier
    Space debris represents a growing threat for both current spacecraft and future launches. This is exceptionally alarming in the case of low Earth orbits, where chain impacts of existing debris generate even more fragments, increasing the probability of further collisions. The now defunct satellite Envisat represents one of the largest objects classified as space debris. The e.Deorbit mission will demonstrate active debris removal (ADR) technology to successfully decommission Envisat and other non-functional target spacecraft in orbit. Relative navigation solutions shall be achieved using image processing algorithms, which implies the detection and matching of two-dimensional regions of interest. In this work, multiple pattern recognition techniques are investigated for the detection and description of these features. This analysis of feature perception is achieved for the first time in the context of space non-cooperative rendezvous (NCRV) across two different modalities: the visible (0.39-0.70 μm) and the thermal infrared (8-14 μm). The assessed algorithms are implemented in a dedicated, space-appropriate hardware processor to benchmark their real-time capabilities.