Characterizing Satellite Geometry via Accelerated 3D Gaussian Splatting
The accelerating deployment of spacecraft in orbit has generated interest in on-orbit servicing (OOS), inspection of spacecraft, and active debris removal (ADR). Such missions require precise rendezvous and proximity operations in the vicinity of non-cooperative, possibly unknown, resident space obj...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-02-01
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| Series: | Aerospace |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2226-4310/11/3/183 |
| _version_ | 1797242462512611328 |
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| author | Van Minh Nguyen Emma Sandidge Trupti Mahendrakar Ryan T. White |
| author_facet | Van Minh Nguyen Emma Sandidge Trupti Mahendrakar Ryan T. White |
| author_sort | Van Minh Nguyen |
| collection | DOAJ |
| description | The accelerating deployment of spacecraft in orbit has generated interest in on-orbit servicing (OOS), inspection of spacecraft, and active debris removal (ADR). Such missions require precise rendezvous and proximity operations in the vicinity of non-cooperative, possibly unknown, resident space objects. Safety concerns with manned missions and lag times with ground-based control necessitate complete autonomy. This requires robust characterization of the target’s geometry. In this article, we present an approach for mapping geometries of satellites on orbit based on 3D Gaussian splatting that can run on computing resources available on current spaceflight hardware. We demonstrate model training and 3D rendering performance on a hardware-in-the-loop satellite mock-up under several realistic lighting and motion conditions. Our model is shown to be capable of training on-board and rendering higher quality novel views of an unknown satellite nearly 2 orders of magnitude faster than previous NeRF-based algorithms. Such on-board capabilities are critical to enable downstream machine intelligence tasks necessary for autonomous guidance, navigation, and control tasks. |
| first_indexed | 2024-04-24T18:39:36Z |
| format | Article |
| id | doaj.art-2d8d0de0178240748b4d1e379c409b5a |
| institution | Directory Open Access Journal |
| issn | 2226-4310 |
| language | English |
| last_indexed | 2024-04-24T18:39:36Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj.art-2d8d0de0178240748b4d1e379c409b5a2024-03-27T13:15:35ZengMDPI AGAerospace2226-43102024-02-0111318310.3390/aerospace11030183Characterizing Satellite Geometry via Accelerated 3D Gaussian SplattingVan Minh Nguyen0Emma Sandidge1Trupti Mahendrakar2Ryan T. White3NEural TransmissionS (NETS) Lab, Florida Institute of Technology, Melbourne, FL 32901, USANEural TransmissionS (NETS) Lab, Florida Institute of Technology, Melbourne, FL 32901, USANEural TransmissionS (NETS) Lab, Florida Institute of Technology, Melbourne, FL 32901, USANEural TransmissionS (NETS) Lab, Florida Institute of Technology, Melbourne, FL 32901, USAThe accelerating deployment of spacecraft in orbit has generated interest in on-orbit servicing (OOS), inspection of spacecraft, and active debris removal (ADR). Such missions require precise rendezvous and proximity operations in the vicinity of non-cooperative, possibly unknown, resident space objects. Safety concerns with manned missions and lag times with ground-based control necessitate complete autonomy. This requires robust characterization of the target’s geometry. In this article, we present an approach for mapping geometries of satellites on orbit based on 3D Gaussian splatting that can run on computing resources available on current spaceflight hardware. We demonstrate model training and 3D rendering performance on a hardware-in-the-loop satellite mock-up under several realistic lighting and motion conditions. Our model is shown to be capable of training on-board and rendering higher quality novel views of an unknown satellite nearly 2 orders of magnitude faster than previous NeRF-based algorithms. Such on-board capabilities are critical to enable downstream machine intelligence tasks necessary for autonomous guidance, navigation, and control tasks.https://www.mdpi.com/2226-4310/11/3/183spacecraft inspectionunknown resident space objecton-orbit servicingspacecraft characterizationactive debris removalrendezvous proximity operations |
| spellingShingle | Van Minh Nguyen Emma Sandidge Trupti Mahendrakar Ryan T. White Characterizing Satellite Geometry via Accelerated 3D Gaussian Splatting Aerospace spacecraft inspection unknown resident space object on-orbit servicing spacecraft characterization active debris removal rendezvous proximity operations |
| title | Characterizing Satellite Geometry via Accelerated 3D Gaussian Splatting |
| title_full | Characterizing Satellite Geometry via Accelerated 3D Gaussian Splatting |
| title_fullStr | Characterizing Satellite Geometry via Accelerated 3D Gaussian Splatting |
| title_full_unstemmed | Characterizing Satellite Geometry via Accelerated 3D Gaussian Splatting |
| title_short | Characterizing Satellite Geometry via Accelerated 3D Gaussian Splatting |
| title_sort | characterizing satellite geometry via accelerated 3d gaussian splatting |
| topic | spacecraft inspection unknown resident space object on-orbit servicing spacecraft characterization active debris removal rendezvous proximity operations |
| url | https://www.mdpi.com/2226-4310/11/3/183 |
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