Towards Bridging the Space Domain Gap for Satellite Pose Estimation using Event Sensing

TL;DR

This paper presents an event-based satellite pose estimation method trained solely on synthetic data, demonstrating effective generalization to real extreme space environments without domain adaptation.

Contribution

The authors introduce a novel event-based satellite pose estimation technique trained on synthetic data, capable of generalizing to real space scenarios without domain adaptation.

Findings

Method successfully generalizes to real event data from space-like conditions.

Synthetic training with basic augmentation improves robustness to sensor noise.

Event sensing reduces the need for extensive target domain data.

Abstract

Deep models trained using synthetic data require domain adaptation to bridge the gap between the simulation and target environments. State-of-the-art domain adaptation methods often demand sufficient amounts of (unlabelled) data from the target domain. However, this need is difficult to fulfil when the target domain is an extreme environment, such as space. In this paper, our target problem is close proximity satellite pose estimation, where it is costly to obtain images of satellites from actual rendezvous missions. We demonstrate that event sensing offers a promising solution to generalise from the simulation to the target domain under stark illumination differences. Our main contribution is an event-based satellite pose estimation technique, trained purely on synthetic event data with basic data augmentation to improve robustness against practical (noisy) event sensors. Underpinning our method is a novel dataset with carefully calibrated ground truth, comprising of real event data obtained by emulating satellite rendezvous scenarios in the lab under drastic lighting conditions. Results on the dataset showed that our event-based satellite pose estimation method, trained only on synthetic data without adaptation, could generalise to the target domain effectively.