ChiNet: deep recurrent convolutional learning for multimodal spacecraft pose estimation

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dc.contributor.authorRondao, Duarte
dc.contributor.authorAouf, Nabil
dc.contributor.authorRichardson, Mark A.
dc.date.accessioned2022-08-01T10:53:37Z
dc.date.available2022-08-01T10:53:37Z
dc.date.issued2022-07-22
dc.description.abstractThis paper presents an innovative deep learning pipeline which estimates the relative pose of a spacecraft by incorporating the temporal information from a rendezvous sequence. It leverages the performance of long short-term memory (LSTM) units in modelling sequences of data for the processing of features extracted by a convolutional neural network (CNN) backbone. Three distinct training strategies, which follow a coarse-to-fine funnelled approach, are combined to facilitate feature learning and improve end-to-end pose estimation by regression. The capability of CNNs to autonomously ascertain feature representations from images is exploited to fuse thermal infrared data with electro-optical red-green-blue (RGB) inputs, thus mitigating the effects of artifacts from imaging space objects in the visible wavelength. Each contribution of the proposed framework, dubbed ChiNet, is demonstrated on a synthetic dataset, and the complete pipeline is validated on experimental data.en_UK
dc.identifier.citationRondao D, Aouf N, Richardson MA. (2023) ChiNet: deep recurrent convolutional learning for multimodal spacecraft pose estimation. IEEE Transactions on Aerospace and Electronic Systems, Volume 59, Issue 2, April 2023, pp. 937-949en_UK
dc.identifier.eissn1557-9603
dc.identifier.issn0018-9251
dc.identifier.urihttps://doi.org/10.1109/TAES.2022.3193085
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/18261
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectFeature extractionen_UK
dc.subjectPose estimationen_UK
dc.subjectSpace vehiclesen_UK
dc.subjectSolid modelingen_UK
dc.subjectTask analysisen_UK
dc.subjectEstimationen_UK
dc.subjectConvolutional neural networksen_UK
dc.titleChiNet: deep recurrent convolutional learning for multimodal spacecraft pose estimationen_UK
dc.typeArticleen_UK

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