  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.
