解决空间机器人单目视觉定位初始化难题，提升复杂场景下建图精度。
Initialization of Monocular Visual Navigation for Autonomous Agents Using Modified Structure from Small Motion
- 基于改进的微小运动结构法，构建优化因子图实现稳定初始化
- 在翻滚卫星图像序列上验证，定位误差显著优于现有方法
- 适合航天器巡检等弱纹理、动态光照环境下的自主导航任务
我们提出一种独立的单目视觉同时定位与地图构建（vSLAM）初始化流程，专用于自主空间机器人。该方法基于先进的因子图优化框架，将微小运动结构法（SfSM）扩展至应对弱透视投影、中心对准运动带来的深度模糊问题，以及主导平面几何引起的运动估计退化，并缓解动态光照对视觉信息的破坏。我们在模拟真实卫星巡检的翻滚航天器图像序列上验证了该方法的有效性，结果表明其在单目初始化性能上优于现有技术。
原文摘要 · Abstract (English)
We propose a standalone monocular visual Simultaneous Localization and Mapping (vSLAM) initialization pipeline for autonomous space robots. Our method, a state-of-the-art factor graph optimization pipeline, extends Structure from Small Motion (SfSM) to robustly initialize a monocular agent in spacecraft inspection trajectories, addressing visual estimation challenges such as weak-perspective projection and center-pointing motion, which exacerbates the bas-relief ambiguity, dominant planar geometry, which causes motion estimation degeneracies in classical Structure from Motion, and dynamic illumination conditions, which reduce the survivability of visual information. We validate our approach on realistic, simulated satellite inspection image sequences with a tumbling spacecraft and demonstrate the method's effectiveness over existing monocular initialization procedures.
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