A Robust RANSAC-Based Planet Radius Estimation for Onboard Visual Based Navigation
Individual spacecraft manual navigation by human operators from ground station is expected to be an emerging problem as the number of spacecraft for space exploration increases. Hence, as an attempt to reduce the burden to control multiple spacecraft, future missions will employ smart spacecraft abl...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-07-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/20/14/4041 |
| _version_ | 1797561790878449664 |
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| author | Francesco de Gioia Gabriele Meoni Gianluca Giuffrida Massimiliano Donati Luca Fanucci |
| author_facet | Francesco de Gioia Gabriele Meoni Gianluca Giuffrida Massimiliano Donati Luca Fanucci |
| author_sort | Francesco de Gioia |
| collection | DOAJ |
| description | Individual spacecraft manual navigation by human operators from ground station is expected to be an emerging problem as the number of spacecraft for space exploration increases. Hence, as an attempt to reduce the burden to control multiple spacecraft, future missions will employ smart spacecraft able to navigate and operate autonomously. Recently, image-based optical navigation systems have proved to be promising solutions for inexpensive autonomous navigation. In this paper, we propose a robust image processing pipeline for estimating the center and radius of planets and moons in an image taken by an on-board camera. Our custom image pre-processing pipeline is tailored for resource-constrained applications, as it features a computationally simple processing flow with a limited memory footprint. The core of the proposed pipeline is a best-fitting model based on the RANSAC algorithm that is able to handle images corrupted with Gaussian noise, image distortions, and frame drops. We report processing time, pixel-level error of estimated body center and radius and the effect of noise on estimated body parameters for a dataset of synthetic images. |
| first_indexed | 2024-03-10T18:19:38Z |
| format | Article |
| id | doaj.art-3473498ce36642448e467ca81124a066 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T18:19:38Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-3473498ce36642448e467ca81124a0662023-11-20T07:23:23ZengMDPI AGSensors1424-82202020-07-012014404110.3390/s20144041A Robust RANSAC-Based Planet Radius Estimation for Onboard Visual Based NavigationFrancesco de Gioia0Gabriele Meoni1Gianluca Giuffrida2Massimiliano Donati3Luca Fanucci4Department of Information Engineering, University of Pisa Via Girolamo Caruso 16, 56122 Pisa, PI, ItalyIngeniArs S.r.l; Via Ponte a Piglieri 8, 56121 Pisa, PI, ItalyDepartment of Information Engineering, University of Pisa Via Girolamo Caruso 16, 56122 Pisa, PI, ItalyDepartment of Information Engineering, University of Pisa Via Girolamo Caruso 16, 56122 Pisa, PI, ItalyDepartment of Information Engineering, University of Pisa Via Girolamo Caruso 16, 56122 Pisa, PI, ItalyIndividual spacecraft manual navigation by human operators from ground station is expected to be an emerging problem as the number of spacecraft for space exploration increases. Hence, as an attempt to reduce the burden to control multiple spacecraft, future missions will employ smart spacecraft able to navigate and operate autonomously. Recently, image-based optical navigation systems have proved to be promising solutions for inexpensive autonomous navigation. In this paper, we propose a robust image processing pipeline for estimating the center and radius of planets and moons in an image taken by an on-board camera. Our custom image pre-processing pipeline is tailored for resource-constrained applications, as it features a computationally simple processing flow with a limited memory footprint. The core of the proposed pipeline is a best-fitting model based on the RANSAC algorithm that is able to handle images corrupted with Gaussian noise, image distortions, and frame drops. We report processing time, pixel-level error of estimated body center and radius and the effect of noise on estimated body parameters for a dataset of synthetic images.https://www.mdpi.com/1424-8220/20/14/4041RANSACvisual based navigationoptical navigationradius estimationcircle fittingimage processing |
| spellingShingle | Francesco de Gioia Gabriele Meoni Gianluca Giuffrida Massimiliano Donati Luca Fanucci A Robust RANSAC-Based Planet Radius Estimation for Onboard Visual Based Navigation Sensors RANSAC visual based navigation optical navigation radius estimation circle fitting image processing |
| title | A Robust RANSAC-Based Planet Radius Estimation for Onboard Visual Based Navigation |
| title_full | A Robust RANSAC-Based Planet Radius Estimation for Onboard Visual Based Navigation |
| title_fullStr | A Robust RANSAC-Based Planet Radius Estimation for Onboard Visual Based Navigation |
| title_full_unstemmed | A Robust RANSAC-Based Planet Radius Estimation for Onboard Visual Based Navigation |
| title_short | A Robust RANSAC-Based Planet Radius Estimation for Onboard Visual Based Navigation |
| title_sort | robust ransac based planet radius estimation for onboard visual based navigation |
| topic | RANSAC visual based navigation optical navigation radius estimation circle fitting image processing |
| url | https://www.mdpi.com/1424-8220/20/14/4041 |
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