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Abstract
Vision-based relative navigation technology is a key enabler of several areas of the space industry such as on-orbit servicing, space debris removal, and formation flying. A particularly demanding scenario is navigating relative to a non-cooperative target that does not offer any navigational aid and is unable to stabilise its attitude. This research integrates a convolutional neural network (CNN) and an EPnP-solver in a pose initialisation system. The system's performance is benchmarked on images gathered from the European Proximity Operations Simulator EPOS 2.0 laboratory. A synthetic dataset is generated using Blender as a rendering engine. A segmentation-based pose estimation CNN is trained using the synthetic dataset and the resulting pose estimation performance is evaluated on a set of real images gathered from the cameras of the EPOS 2.0 robotic close-range relative navigation laboratory. It is demonstrated that a synthetic-image-trained CNN-based pose estimation pipeline is able to successfully perform in a close-range visual relative navigation setting on real camera images of a 6-facet symmetrical spacecraft.
Original language | English |
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Pages (from-to) | 2824-2844 |
Number of pages | 21 |
Journal | Advances in Space Research |
Volume | 72 |
Issue number | 7 |
Early online date | 17 Feb 2023 |
DOIs | |
Publication status | Published - 1 Oct 2023 |
Keywords
- close-range relative navigation
- pose estimation
- symmetric uncooperative target
- monocular camera
- convolutional network
- domain randomisation
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Dive into the research topics of 'Domain randomisation and CNN-based keypoint-regressing pose initialisation for relative navigation with uncooperative finite-symmetric spacecraft targets using monocular camera images'. Together they form a unique fingerprint.Projects
- 1 Finished
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Stardust-R (Stardust Reloaded) H2020 MCSA ITN 2018
Vasile, M. (Principal Investigator), Feng, J. (Co-investigator), Fossati, M. (Co-investigator), Maddock, C. (Co-investigator), Minisci, E. (Co-investigator) & Riccardi, A. (Co-investigator)
European Commission - Horizon Europe + H2020
1/01/19 → 31/12/22
Project: Research