We propose a standalone monocular visual Simultaneous Localization and Mapping (vSLAM) initialization pipeline for autonomous robots in space. Our method, a state-of-the-art factor graph optimization pipeline, enhances classical Structure from Small Motion (SfSM) to robustly initialize a monocular agent in weak-perspective projection scenes. Furthermore, it overcomes visual estimation challenges introduced by spacecraft inspection trajectories, such as: center-pointing motion, which exacerbates the bas-relief ambiguity, and the presence of a dominant plane in the scene, which causes motion estimation degeneracies in classical Structure from Motion (SfM). We validate our method on realistic, simulated satellite inspection images exhibiting weak-perspective projection, and we demonstrate its effectiveness and improved performance compared to other monocular initialization procedures.

翻译：本文提出了一种适用于空间自主机器人的独立单目视觉同时定位与建图初始化流程。该方法采用先进的因子图优化流程，通过增强经典的小运动结构恢复技术，能够在弱透视投影场景中鲁棒地初始化单目智能体。此外，该方法克服了航天器巡检轨迹带来的视觉估计挑战，例如：指向中心的运动会加剧浮雕歧义问题，而场景中主导平面的存在会导致经典运动恢复结构方法中的运动估计退化。我们在呈现弱透视投影特性的仿真卫星巡检图像上验证了本方法，并证明了相较于其他单目初始化流程，本方法具有更高的有效性与改进的性能。