Characterizing Satellite Geometry via Accelerated 3D Gaussian Splatting
Van Minh Nguyen Emma Sandidge Trupti Mahendrakar Ryan T. White
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.