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Cited by 32Space Satellite Systems and ControlDomain Adaptation and Few-Shot LearningSpacecraft Dynamics and Control

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

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Jawaid, Mohsi, Elms, Ethan, Latif, Yasir, Chin, Tat-Jun·2023·2023 IEEE International Conference on Robotics and Automation (ICRA)
DOI 10.1109/icra48891.2023.10160531
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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.

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  • A domain gap exists between real and synthetically generated space-borne datasets that affects the performance and accuracy of learning-based architectures.

    “Fourth, the domain gap exists between real and synthetically generated space-borne datasets that affect the performance and accuracy of learning-based architectures [21,23].”

    From Transformer Network-Aided Relative Pose Estimation for Non-cooperative Spacecraft Using Vision Sensor (2024) · cites this paper as [21]

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Related

Authors
Jawaid, M., Elms, E., Latif, Y., & Chin, T.-J.
Affiliations
Australian Institute for Machine Learning (AIML),Sentient Satellites Laboratory (SSL),Adelaide,Australia
ORCID
Venue
2023 IEEE International Conference on Robotics and Automation (ICRA)
Publisher
IEEE
Published
2023-05-29
Pages
11866-11873
ISSN
1050-4729 (ISSN-L)
DOI
10.1109/icra48891.2023.10160531
OpenAlex
W4383097421
Semantic Scholar
9af32c30b2a8ec7ae507915b2016d9816f73dfb2
Cited by
32
References
52
Citations by year
2026: 3 · 2025: 13 · 2024: 14 · 2023: 2
FWCI
16.88
Type
conference paper
Language
en
Open access
Licence
Locations
  • Proceedings - IEEE International Conference on Robotics and Automation/Proceedings (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
Northrop Grumman
Topics
  • Physical Sciences › Engineering › Aerospace Engineering › Space Satellite Systems and Control
  • Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
  • Physical Sciences › Engineering › Aerospace Engineering › Spacecraft Dynamics and Control
Keywords
satellite pose estimation, event cameras, domain adaptation, sim-to-real transfer, satellite rendezvous, synthetic data, data augmentation, illumination variation, ground truth dataset

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