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Deep models trained using synthetic data require domain adaptation to bridgethe gap between the simulation and target environments. State-of-the-art domainadaptation methods often demand sufficient amounts of (unlabelled) data fromthe target domain. However, this need is difficult to fulfil when the targetdomain is an extreme environment, such as space. In this paper, our targetproblem is close proximity satellite pose estimation, where it is costly toobtain images of satellites from actual rendezvous missions. We demonstratethat event sensing offers a promising solution to generalise from thesimulation to the target domain under stark illumination differences. Our maincontribution is an event-based satellite pose estimation technique, trainedpurely on synthetic event data with basic data augmentation to improverobustness against practical (noisy) event sensors. Underpinning our method isa novel dataset with carefully calibrated ground truth, comprising of realevent data obtained by emulating satellite rendezvous scenarios in the labunder drastic lighting conditions. Results on the dataset showed that ourevent-based satellite pose estimation method, trained only on synthetic datawithout adaptation, could generalise to the target domain effectively.

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