Relative visual navigation based on CNN in a proximity operation space mission
A. D’Ortona, G. Daddidownload PDF
Abstract. This article explores a solution utilizing a convolutional neural network (CNN) to simulate robust monocular visual navigation during proximity operations of a space mission, where a precise determination of relative pose is crucial for mission safety. This operation involves closely observing a spacecraft with a CubeSat under challenging illumination conditions. The methodology involves generating a dataset using Blender software and training a Mask-CNN with a ResNet-50 architecture to identify relevant features representing the target’s 3D model. The dataset’s ground truth is obtained through an inverse Perspective-n-Point (PnP) problem. Overall, this work provides valuable insights into the potential of deep learning-based visual navigation techniques for enhancing space mission operations.
Navigation, Space, CNN
Published online 9/1/2023, 6 pages
Copyright © 2023 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: A. D’Ortona, G. Daddi, Relative visual navigation based on CNN in a proximity operation space mission, Materials Research Proceedings, Vol. 33, pp 9-14, 2023
The article was published as article 2 of the book Aerospace Science and Engineering
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