In recent years, researchers have proposed a variety of approaches to tackle the problem of orbital debris. Debris targets are diverse, and prior knowledge may be limited, with unknown uncooperative debris targets being the most challenging category. A crucial portion of any debris capture scenario is the observation phase during the approach. During this phase, the chaser spacecraft attempts to learn as much as possible about the target by using remote sensing, of which relative pose is of particular interest, to enable the advancement of the mission towards eventual capture.

This research utilizes the fusion of data from a scanning LiDAR with a long-wavelength infrared camera to estimate the relative pose of an unknown uncooperative target. Two separate bespoke pose estimation algorithms, color-ICP and Feature Matching, were developed and tested with laboratory experiments mimicking the close-approach phase with a target under various lighting conditions and relative motion rates. The color-ICP algorithm uses a thermal infrared-infused color-assisted Generalized Iterative Closest Points method, while the Feature Matching algorithm uses computer vision on LiDAR point-infused thermal images to track BRISK feature points in each frame to estimate pose.

In general, the color-ICP algorithm delivered more accurate results throughout the range of experiments, though the fusion was slightly detrimental while the target is being heated or cooled. The Feature Matching algorithm contains a large amount of tunable parameters, making the estimation highly sensitive yet
versatile, demonstrating that harsh lighting conditions can be mitigated with accurate features tracked after the implementation of image processing techniques. Overall, the end product shows promise as a light-agnostic remote sensing and pose estimation solution.

This research contributes to the advancement of active debris removal theory and explores two promising avenues for LiDAR-infrared sensor fusion for pose estimation, laying the groundwork for further iterations exploring this sensor pairing. The resulting use case is a conceivable scenario in which these sensors work together to supplement individual strengths and mitigate disadvantages throughout the approach phase of a debris removal mission.