SatSplatYOLO: 3D Gaussian Splatting-based Virtual Object Detection Ensembles for Satellite Feature Recognition
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.
在轨服务（OOS）、航天器检查以及主动太空碎片清除（ADR）等任务需要在非合作、可能未知的在轨空间对象附近进行精确的交会和近邻操作。有人任务的安全问题以及地面控制的延迟时间要求完全自主性。在本文中，我们介绍了一种方法，用于绘制未知、非合作卫星在轨组件的几何形状和高置信度检测。我们实现了加速的3D高斯涂抹来学习卫星的3D表示，渲染目标的虚拟视图，并在虚拟视图上集成YOLOv5对象检测器，从而实现可靠、准确和精确的卫星组件检测。整个管道能够在机载系统上运行，并支持自主导航、导航和控制任务所必需的下游机器智能任务。