Deep learning-based spacecraft pose estimation for the pre-capture phase scenario

Deep learning-based spacecraft pose estimation for the pre-capture phase scenario

Roman Prokazov

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Abstract. The increasing population of space debris in Low-Earth Orbit (LEO) poses a significant threat to operational satellites and future space endeavors. To address this challenge, leading aerospace companies worldwide are developing on-orbit servicing and debris removal satellites. These servicer satellites will be capable of complex orbital operations, such as capturing tumbling defunct spacecraft. A fundamental requirement for the success of such missions is the development of accurate spacecraft pose estimation, which provides the servicer’s guidance and control system with precise information about the target spacecraft’s attitude. This paper addresses the study of such a pipeline using deep learning and classical computer vision algorithms.

Deep Learning, Computer Vision, Spacecraft Pose Estimation, Synthetic Data

Published online 6/1/2024, 5 pages
Copyright © 2024 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA

Citation: Roman Prokazov, Deep learning-based spacecraft pose estimation for the pre-capture phase scenario, Materials Research Proceedings, Vol. 42, pp 178-182, 2024


The article was published as article 39 of the book Aerospace Science and Engineering

Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

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