We address the problem of estimating the relative 6D pose, i.e., position and
orientation, of a target spacecraft, from a monocular image, a key capability
for future autonomous Rendezvous and Proximity Operations. Due to the
difficulty of acquiring large sets of real images, spacecraft pose estimation
networks are exclusively trained on synthetic ones. However, because those
images do not capture the illumination conditions encountered in orbit, pose
estimation networks face a domain gap problem, i.e., they do not generalize to
real images. Our work introduces a method that bridges this domain gap. It
relies on a novel, end-to-end, neural-based architecture as well as a novel
learning strategy. This strategy improves the domain generalization abilities
of the network through multi-task learning and aggressive data augmentation
policies, thereby enforcing the network to learn domain-invariant features. We
demonstrate that our method effectively closes the domain gap, achieving
state-of-the-art accuracy on the widespread SPEED+ dataset. Finally, ablation
studies assess the impact of key components of our method on its generalization
abilities.

我们提出了一种通过多任务学习和积极的数据增强策略来改善网络的领域泛化能力，从而实现相对 6D 姿态和空间飞行器姿态估计的跨领域问题。