Reference : CubeSat-CDT: A Cross-Domain Dataset for 6-DoF Trajectory Estimation of a Symmetric Sp...

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a journal
	Discipline(s) :	Engineering, computing & technology : Computer science
	To cite this reference:	http://hdl.handle.net/10993/52237

Title :	CubeSat-CDT: A Cross-Domain Dataset for 6-DoF Trajectory Estimation of a Symmetric Spacecraft
Language :	English
Author, co-author :	Mohamed Ali, Mohamed Adel [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CVI2 >]
	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 >]
	Ortiz Del Castillo, Miguel [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 >]
Publication date :	2022
Journal title :	Proceedings of the 17th European Conference on Computer Vision Workshops (ECCVW 2022)
Peer reviewed :	Yes
Audience :	International
Event name :	European Conference on Computer Vision Workshops
Event date :	October 23-27, 2022
Keywords :	[en] Computer Vision ; Deep Learning ; Space Situational Awareness
Abstract :	[en] 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 three data sources incorporate the same 1U CubeSat and share the same camera intrinsic parameters. In ad- dition, we conduct experiments to show the characteristics of the dataset using a novel and efficient spacecraft trajectory estimation method, that leverages the information provided from the three data domains. Given a video input of a target spacecraft, the proposed end-to-end approach re- lies on a Temporal Convolutional Network that enforces the inter-frame coherence of the estimated 6-Degree-of-Freedom spacecraft poses. The pipeline is decomposed into two stages; first, spatial features are ex- tracted from each frame in parallel; second, these features are lifted to the space of camera poses while preserving temporal information. Our re- sults highlight the importance of addressing the domain gap problem to propose reliable solutions for close-range autonomous relative navigation between spacecrafts. Since the nature of the data used during training impacts directly the performance of the final solution, the CubeSat-CDT dataset is provided to advance research into this direction.
Research centres :	Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Computer Vision Imaging & Machine Intelligence (CVI²)
Target :	Researchers ; Professionals ; Students ; General public
Permalink :	http://hdl.handle.net/10993/52237
FnR project :	FnR ; FNR14755859 > Djamila Aouada > MEET-A > Multi-modal Fusion Of Electro-optical Sensors For Spacecraft Pose Estimation Towards Autonomous In-orbit Operations > 01/01/2021 > 31/12/2023 > 2020

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	Fulltext file(s): File Commentary Version Size Access Open access CubeSat-CDTA Cross-Domain Dataset for 6-DoF Trajectory Estimation of a Symmetric Spacecraft.pdf Publisher postprint 3.52 MB View/Open

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