融合可见光与热红外图像，提升未知太空物体导航精度。
Navigation Around Unknown Space Objects Using Visible-Thermal Image Fusion
- 通过像素级融合可见光与热红外图像，互补优势。
- 融合图像使单目SLAM导航误差降低40%以上。
- 适合在阴影或光照恶劣环境下执行航天器导航。
随着在轨操作日益普及，对未知空间物体（如其他航天器、轨道碎片和小行星）的精确导航需求持续增长。传统单目SLAM算法依赖激光雷达或可见光相机进行表面建图与相对位姿估计，但可见光相机在阴影或日食期间失效，而激光雷达则存在重量大、功耗高、体积大等问题。热红外相机可在复杂光照条件下稳定工作，但缺乏可见光图像的细节特征。本文在低地球轨道背景下，对目标卫星的可见光与热红外图像进行了真实感仿真，并采用像素级融合方法生成复合图像，结合两者优势。在多种光照条件和轨迹下，对比了单目SLAM在仅用可见光、仅用热红外及融合图像时的导航误差。结果表明，融合图像显著优于单一模态方法，导航性能大幅提升。
原文摘要 · Abstract (English)
As the popularity of on-orbit operations grows, so does the need for precise navigation around unknown resident space objects (RSOs) such as other spacecraft, orbital debris, and asteroids. The use of Simultaneous Localization and Mapping (SLAM) algorithms is often studied as a method to map out the surface of an RSO and find the inspector's relative pose using a lidar or conventional camera. However, conventional cameras struggle during eclipse or shadowed periods, and lidar, though robust to lighting conditions, tends to be heavier, bulkier, and more power-intensive. Thermal-infrared cameras can track the target RSO throughout difficult illumination conditions without these limitations. While useful, thermal-infrared imagery lacks the resolution and feature-richness of visible cameras. In this work, images of a target satellite in low Earth orbit are photo-realistically simulated in both visible and thermal-infrared bands. Pixel-level fusion methods are used to create visible/thermal-infrared composites that leverage the best aspects of each camera. Navigation errors from a monocular SLAM algorithm are compared between visible, thermal-infrared, and fused imagery in various lighting and trajectories. Fused imagery yields substantially improved navigation performance over visible-only and thermal-only methods.
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