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.

翻译：本文研究从单目图像中估计目标航天器相对6D姿态（即位置与朝向）的问题，这是未来自主交会对接与近距离操作的关键技术。由于获取大规模真实图像数据存在困难，现有航天器姿态估计网络均完全基于合成图像进行训练。然而，由于合成图像无法准确模拟在轨光照条件，姿态估计网络面临领域差异问题——即无法有效泛化至真实图像。本研究提出一种弥合该领域差异的新方法，其核心在于构建新颖的端到端神经网络架构，并设计创新的学习策略。该策略通过多任务学习与强化的数据增强策略，提升网络的领域泛化能力，从而迫使网络学习领域不变特征。实验证明，本方法能有效消除领域差异，在广泛使用的SPEED+数据集上达到了最先进的精度。最后，通过消融实验评估了本方法关键组成部分对其泛化能力的影响。