  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, possibly unknown, resident space objects. Safety
concerns with manned missions and lag times with ground-based control
necessitate complete autonomy. In this article, we present an approach for
mapping geometries and high-confidence detection of components of unknown,
non-cooperative satellites on orbit. We implement accelerated 3D Gaussian
splatting to learn a 3D representation of the satellite, render virtual views
of the target, and ensemble the YOLOv5 object detector over the virtual views,
resulting in reliable, accurate, and precise satellite component detections.
The full pipeline capable of running on-board and stand to enable downstream
machine intelligence tasks necessary for autonomous guidance, navigation, and
control tasks.
