Image-based attitude determination of co-orbiting satellites enhanced with deep learning technologies
Image-based attitude determination of co-orbiting satellites enhanced with deep learning technologies
Active debris removal missions pose demanding guidance, navigation and con-trol requirements. We present a novel approach which adopts deep learning technologies to the problem of attitude determination of an uncooperative debris satellite of a-priori unknown geometry. A siamese convolutional neural network is developed, which detects and tracks inherently useful landmarks from sensor data, after training upon synthetic datasets of visual, LiDAR or RGB-D data. The method is capable of real-time performance while significantly improving upon conventional computer vision-based approaches, and generalises well to previously unseen object geometries, enabling this approach to be a feasible so-lution for guidance in active debris removal missions. The performance of the algorithm and its sensitivity to model parameters are analysed via numerical simulation.
3164-3182
American Astronautical Society
Guthrie, Benjamin, Felix
03652589-afe9-4d34-a4fd-831cb1562c73
Kim, Minkwan
18ed9a6f-484f-4a7c-bf24-b630938c1acc
Urrutxua, Hodei
35a0462a-dec0-44a7-95d9-93bee6ab1d29
Hare, Jonathon
65ba2cda-eaaf-4767-a325-cd845504e5a9
Guthrie, Benjamin, Felix
03652589-afe9-4d34-a4fd-831cb1562c73
Kim, Minkwan
18ed9a6f-484f-4a7c-bf24-b630938c1acc
Urrutxua, Hodei
35a0462a-dec0-44a7-95d9-93bee6ab1d29
Hare, Jonathon
65ba2cda-eaaf-4767-a325-cd845504e5a9
Guthrie, Benjamin, Felix, Kim, Minkwan, Urrutxua, Hodei and Hare, Jonathon
(2021)
Image-based attitude determination of co-orbiting satellites enhanced with deep learning technologies.
In Advances in the Astronautical Sciences.
vol. 175,
American Astronautical Society.
.
(In Press)
Record type:
Conference or Workshop Item
(Paper)
Abstract
Active debris removal missions pose demanding guidance, navigation and con-trol requirements. We present a novel approach which adopts deep learning technologies to the problem of attitude determination of an uncooperative debris satellite of a-priori unknown geometry. A siamese convolutional neural network is developed, which detects and tracks inherently useful landmarks from sensor data, after training upon synthetic datasets of visual, LiDAR or RGB-D data. The method is capable of real-time performance while significantly improving upon conventional computer vision-based approaches, and generalises well to previously unseen object geometries, enabling this approach to be a feasible so-lution for guidance in active debris removal missions. The performance of the algorithm and its sensitivity to model parameters are analysed via numerical simulation.
More information
Submitted date: 31 July 2020
Accepted/In Press date: 21 May 2021
Venue - Dates:
AAS/AIAA Astrodynamics Specialist Conference, Online, 2020-08-09 - 2020-08-12
Identifiers
Local EPrints ID: 451304
URI: http://eprints.soton.ac.uk/id/eprint/451304
PURE UUID: 98d1235a-e8aa-481c-80e6-29f9ae561a9f
Catalogue record
Date deposited: 20 Sep 2021 16:31
Last modified: 17 Mar 2024 03:33
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Contributors
Author:
Benjamin, Felix Guthrie
Author:
Hodei Urrutxua
Author:
Jonathon Hare
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