On-orbit proximity operations in space rendezvous, docking and debris removal
require precise and robust 6D pose estimation under a wide range of lighting
conditions and against highly textured background, i.e., the Earth. This paper
investigates leveraging deep learning and photorealistic rendering for
monocular pose estimation of known uncooperative spacecrafts. We first present
a simulator built on Unreal Engine 4, named URSO, to generate labeled images of
spacecrafts orbiting the Earth, which can be used to train and evaluate neural
networks. Secondly, we propose a deep learning framework for pose estimation
based on orientation soft classification, which allows modelling orientation
ambiguity as a mixture of Gaussians. This framework was evaluated both on URSO
datasets and the ESA pose estimation challenge. In this competition, our best
model achieved 3rd place on the synthetic test set and 2nd place on the real
test set. Moreover, our results show the impact of several architectural and
training aspects, and we demonstrate qualitatively how models learned on URSO
datasets can perform on real images from space.

利用深度学习和逼真的渲染技术进行空间交会、对接和空间碎片清理中的六自由度姿态估计，通过提出一种面向方向软分类的深度学习框架，通过在 Unreal 引擎 4 上构建模拟器生成标注图像，且在欧洲航天局的姿态估计竞赛中取得了第三名和第二名的优异成绩。