Characterizing Satellite Geometry via Accelerated 3D Gaussian Splatting
This enables robust autonomy for spacecraft in proximity operations, addressing a domain-specific need in space missions.
The paper tackles the problem of autonomously characterizing satellite geometry for on-orbit servicing and debris removal by presenting a 3D Gaussian Splatting approach that trains on-board and renders novel views nearly 100 times faster than previous NeRF-based methods.
The accelerating deployment of spacecraft in orbit have generated interest in on-orbit servicing (OOS), inspection of spacecraft, and active debris removal (ADR). Such missions require precise rendezvous and proximity operations in the vicinity of non-cooperative, possible unknown, resident space objects. Safety concerns with manned missions and lag times with ground-based control necessitate complete autonomy. This requires robust characterization of the target's geometry. In this article, we present an approach for mapping geometries of satellites on orbit based on 3D Gaussian Splatting that can run on computing resources available on current spaceflight hardware. We demonstrate model training and 3D rendering performance on a hardware-in-the-loop satellite mock-up under several realistic lighting and motion conditions. Our model is shown to be capable of training on-board and rendering higher quality novel views of an unknown satellite nearly 2 orders of magnitude faster than previous NeRF-based algorithms. Such on-board capabilities are critical to enable downstream machine intelligence tasks necessary for autonomous guidance, navigation, and control tasks.