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.

中文翻译：

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空间多光谱相对导航的局部特征检测器和描述符的基准测试

摘要 由于视觉同步定位和映射 (VSLAM) 等高效技术的吸引力，基于光学的空间导航是一个越来越受欢迎的领域，这些技术依赖于低成本硬件的自动特征跟踪。然而，低级图像处理算法传统上是针对地面勘探场景进行测量和测试的。本文旨在通过使用定制的合成数据集分析最先进的局部特征检测器和描述符来填补文献中的空白，该数据集模拟具有复杂航天器的非合作交会（NCRV），具有照明变化，旋转和缩放。此外，研究了长波长红外 (LWIR) 算法的性能，作为解决可见光在轨成像固有挑战的可能解决方案，例如漫射光散射和日食条件。Harris、GFTT、DoG、Fast-Hessian、FAST、CenSurE 检测器和 SIFT、SURF、LIOP、ORB、BRISK、FREAK 描述符是 Envisat 图像的基准。结果发现，Fast-Hessian 与 BRISK 的组合是最稳健的，同时仍然能够在低分辨率和采集率设置下运行。对于大型基线，误报率增加，限制了它们在基于模型的策略中的使用。