[en] Orbital debris removal and On-orbit Servicing, Assembly and Manufacturing [OSAM] are the main areas for future robotic space missions. To achieve intelligence and autonomy in these missions and to carry out robot operations, it is essential to have autonomous guidance and navigation, especially vision-based navigation. With recent advances in machine learning, the state-of-the-art Deep Learning [DL] approaches for object detection, and camera pose estimation have advanced to be on par with classical approaches and can be used for target pose estimation during relative navigation scenarios. The state-of-the-art DL-based spacecraft pose estimation approaches are suitable for any known target with significant surface textures. However, it is less applicable in a scenario where the target is a texture-less and symmetric object like rocket nozzles. This paper investigates a novel ellipsoid-based approach combined with convolutional neural networks for texture-less space object pose estimation. Also, this paper presents the dataset for a new texture-less space target, an apogee kick motor, which is used for the study. It includes the synthetic images generated from the simulator developed for rendering synthetic space imagery.
Centre de recherche :
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Computer Vision Imaging & Machine Intelligence (CVI²)
Disciplines :
Ingénierie aérospatiale
Auteur, co-auteur :
RATHINAM, Arunkumar ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CVI2
GAUDILLIERE, Vincent ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CVI2
PAULY, Leo ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CVI2
AOUADA, Djamila ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CVI2
Co-auteurs externes :
no
Langue du document :
Anglais
Titre :
Pose Estimation of a Known Texture-Less Space Target using Convolutional Neural Networks
Date de publication/diffusion :
septembre 2022
Nom de la manifestation :
73rd International Astronautical Congress
Organisateur de la manifestation :
International Astronautical Federation
Lieu de la manifestation :
Paris, France
Date de la manifestation :
18-22 September 2022
Manifestation à portée :
International
Titre de l'ouvrage principal :
73rd International Astronautical Congress, Paris 18-22 September 2022
Projet FnR :
FNR14755859 - Multi-modal Fusion Of Electro-optical Sensors For Spacecraft Pose Estimation Towards Autonomous In-orbit Operations, 2020 (01/01/2021-31/12/2023) - Djamila Aouada
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