Domain Generalization for In-Orbit 6D Pose Estimation
This work solves a domain gap issue for autonomous spacecraft operations, but it is incremental as it builds on existing synthetic-to-real generalization methods.
The paper tackles the problem of domain generalization for 6D pose estimation of spacecraft from monocular images, addressing the gap between synthetic training data and real orbital conditions, and achieves state-of-the-art accuracy on the SPEED+ dataset.
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.