Navigation Around Unknown Space Objects Using Visible-Thermal Image Fusion

TL;DR

This paper proposes a fusion of visible and thermal-infrared images to improve navigation accuracy around unknown space objects, overcoming limitations of individual sensors during challenging lighting conditions.

Contribution

It introduces a pixel-level fusion method for visible and thermal images to enhance SLAM-based navigation around space objects, demonstrating significant performance improvements.

Findings

Fused imagery outperforms single-modality images in navigation accuracy.

Thermal-infrared fusion enables reliable navigation during shadowed or eclipse conditions.

Simulation results show improved SLAM performance with fused images.

Abstract

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.