在轨6D位姿估计的领域泛化
Domain Generalization for In-Orbit 6D Pose Estimation
AI总结：
针对在轨航天器6D位姿估计中合成图像与真实图像间的领域差距问题，提出一种结合端到端神经架构、多任务学习和数据增强的新方法，在SPEED+数据集上达到最先进精度。
摘要：
我们解决了从单目图像估计目标航天器相对6D位姿（即位置和姿态）的问题，这是未来自主交会与近距操作的关键能力。由于获取大量真实图像的困难，航天器位姿估计网络完全在合成图像上训练。然而，由于这些图像未捕捉到在轨遇到的照明条件，位姿估计网络面临领域差距问题，即无法泛化到真实图像。我们的工作引入了一种弥合该领域差距的方法。它依赖于一种新颖的端到端神经架构以及一种新颖的学习策略。该策略通过多任务学习和激进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。我们证明我们的方法有效弥合了领域差距，在广泛使用的SPEED+数据集上实现了最先进的精度。最后，消融研究评估了我们方法的关键组件对其泛化能力的影响。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
