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.

翻译：随着在轨操作日益普及，围绕未知驻留空间物体（如其他航天器、轨道碎片和小行星）进行精确导航的需求也日益增长。同步定位与建图算法常被研究作为绘制RSO表面地图并利用激光雷达或传统相机确定检测器相对位姿的方法。然而，传统相机在星食或阴影时段性能受限，而激光雷达虽对光照条件具有鲁棒性，却往往更重、更笨拙且功耗更高。热红外相机能够在恶劣光照条件下持续跟踪目标RSO，且不受上述限制。尽管有用，热红外图像在分辨率和特征丰富度方面仍不及可见光相机。本研究对低地球轨道目标卫星进行了可见光与热红外波段的照片级真实感模拟。采用像素级融合方法生成可见光/热红外融合图像，以充分发挥各类相机的优势。通过单目SLAM算法，在不同光照条件和轨迹下对比了可见光、热红外及融合图像的导航误差。实验表明，融合图像相较于纯可见光与纯热红外方法，能显著提升导航性能。