A key requirement for autonomous on-orbit proximity operations is the
estimation of a target spacecraft's relative pose (position and orientation).
It is desirable to employ monocular cameras for this problem due to their low
cost, weight, and power requirements. This work presents a novel convolutional
neural network (CNN)-based monocular pose estimation system that achieves
state-of-the-art accuracy with low computational demand. In combination with a
Blender-based synthetic data generation scheme, the system demonstrates the
ability to generalize from purely synthetic training data to real in-space
imagery of the Northrop Grumman Enhanced Cygnus spacecraft. Additionally, the
system achieves real-time performance on low-power flight-like hardware.

本文提出了一种基于卷积神经网络的单目位姿估计系统，结合基于 Blender 的合成数据生成方案，能够从纯合成训练数据泛化到北美洲猫头鹰飞船的真实空间图像，并在低功耗硬件上实现实时性。