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pro vyhledávání: '"Rathinam, Arunkumar"'
In recent years, there has been a growing demand for improved autonomy for in-orbit operations such as rendezvous, docking, and proximity maneuvers, leading to increased interest in employing Deep Learning-based Spacecraft Pose Estimation techniques.
Externí odkaz:
http://arxiv.org/abs/2311.05310
Autor:
Pauly, Leo, Rharbaoui, Wassim, Shneider, Carl, Rathinam, Arunkumar, Gaudilliere, Vincent, Aouada, Djamila
Publikováno v:
Acta Astronautica, Volume 212, November 2023, Pages 339-360
Estimating the pose of an uncooperative spacecraft is an important computer vision problem for enabling the deployment of automatic vision-based systems in orbit, with applications ranging from on-orbit servicing to space debris removal. Following th
Externí odkaz:
http://arxiv.org/abs/2305.07348
Autor:
Gaudillière, Vincent, Pauly, Leo, Rathinam, Arunkumar, Sanchez, Albert Garcia, Musallam, Mohamed Adel, Aouada, Djamila
To automatically localize a target object in an image is crucial for many computer vision applications. To represent the 2D object, ellipse labels have recently been identified as a promising alternative to axis-aligned bounding boxes. This paper fur
Externí odkaz:
http://arxiv.org/abs/2303.02058
In this paper, a discriminator-free adversarial-based Unsupervised Domain Adaptation (UDA) for Multi-Label Image Classification (MLIC) referred to as DDA-MLIC is proposed. Recently, some attempts have been made for introducing adversarial-based UDA m
Externí odkaz:
http://arxiv.org/abs/2301.10611
Autor:
Pauly, Leo, Jamrozik, Michele Lynn, Del Castillo, Miguel Ortiz, Borgue, Olivia, Singh, Inder Pal, Makhdoomi, Mohatashem Reyaz, Christidi-Loumpasefski, Olga-Orsalia, Gaudilliere, Vincent, Martinez, Carol, Rathinam, Arunkumar, Hein, Andreas, Olivares-Mendez, Miguel, Aouada, Djamila
Publikováno v:
International Journal of Aerospace Engineering, vol. 2023, Article ID 9944614, 16 pages, 2023
The use of Deep Learning (DL) algorithms has improved the performance of vision-based space applications in recent years. However, generating large amounts of annotated data for training these DL algorithms has proven challenging. While synthetically
Externí odkaz:
http://arxiv.org/abs/2208.08865
Autor:
Olivares-Mendez, Miguel, Makhdoomi, Mohatashem Reyaz, Yalçın, Barış Can, Bokal, Zhanna, Muralidharan, Vivek, Ortiz Del Castillo, Miguel, Gaudilliere, Vincent, Pauly, Leo, Borgue, Olivia, Alandihallaj, Mohammadamin, Thoemel, Jan, Skrzypczyk, Ernest, Rathinam, Arunkumar, Barad, Kuldeep Rambhai, Shabayek, Abd El Rahman, Hein, Andreas M., Aouada, Djamila, Martinez, Carol
Publikováno v:
In The Journal of Space Safety Engineering December 2023 10(4):509-521
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Akademický článek
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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 a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______2658::35e8722613e11cde25e613866700b820
http://orbilu.uni.lu/handle/10993/52590
http://orbilu.uni.lu/handle/10993/52590
Autor:
Mohamed Ali, Mohamed Adel, Rathinam, Arunkumar, Gaudilliere, Vincent, Ortiz Del Castillo, Miguel, Aouada, Djamila
This paper introduces a new cross-domain dataset, CubeSat- CDT, that includes 21 trajectories of a real CubeSat acquired in a labora- tory setup, combined with 65 trajectories generated using two rendering engines – i.e. Unity and Blender. The thre
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______2658::9d0995159a8e7f3b3a07af8e57ec982e
http://orbilu.uni.lu/handle/10993/52237
http://orbilu.uni.lu/handle/10993/52237