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.

翻译：在轨航天器部署的加速引发了对于在轨服务（OOS）、航天器检测以及主动碎片清除（ADR）的兴趣。此类任务需要在非合作且可能未知的目标空间物体附近执行精确的会合与逼近操作。载人任务的安全性考量以及地面控制的时滞问题要求完全自主运行，这需要对目标几何结构进行稳健表征。本文提出了一种基于三维高斯泼溅（3D Gaussian Splatting）的在轨卫星几何映射方法，该方法可在当前航天硬件上运行的计算资源中执行。我们通过硬件在环卫星模拟器，在多种真实光照和运动条件下演示了模型训练与三维渲染性能。实验表明，该模型可在轨完成训练，并以比先前基于NeRF的算法快近两个数量级的速度渲染未知卫星的高质量新视角图像。此类在轨能力对于实现自主导航、制导与控制所必需的后续机器智能任务至关重要。