We propose a light detection and ranging (LIDAR)‐based relative navigation scheme that is appropriate for uncooperative relative space navigation applications. Our technique combines the encoding power of the three‐dimensional (3D) local descriptors that are matched exploiting a correspondence grouping scheme, with the robust rigid transformation estimation capability of the proposed adaptive recursive filtering techniques. Trials evaluate several current state‐of‐the‐art 3D local descriptors and recursive filtering techniques on a number of both real and simulated scenarios that involve various space objects including satellites and asteroids. Results demonstrate that the proposed architecture affords a 50% odometry accuracy improvement over current solutions, while also affording a low computational burden. From our trials we conclude that the 3D descriptor histogram of distances short (HoD‐S) combined with the adaptive αβ filtering poses the most appealing combination for the majority of the scenarios evaluated, as it combines high quality odometry with a low processing burden.

中文翻译：

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评估基于 LIDAR 的非合作相对空间导航的 3D 局部描述符和递归过滤方案

我们提出了一种基于光检测和测距 (LIDAR) 的相对导航方案，适用于非合作相对空间导航应用。我们的技术结合了利用对应分组方案匹配的三维（3D）局部描述符的编码能力，以及所提出的自适应递归滤波技术的鲁棒刚性变换估计能力。试验在涉及包括卫星和小行星在内的各种空间物体的许多真实和模拟场景中评估了当前最先进的 3D 局部描述符和递归过滤技术。结果表明，与当前解决方案相比，所提出的架构提供了 50% 的测距精度提高，同时还提供了较低的计算负担。