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|Title:||Satellite pose estimation with deep landmark regression and nonlinear pose refinement|
Parra Bustos, A.
|Citation:||Proceedings: 2019 International Conference on Computer Vision Workshops: ICCV 2019, 2019 / pp.2816-2824|
|Series/Report no.:||IEEE International Conference on Computer Vision Workshops|
|Conference Name:||IEEE/CVF International Conference on Computer Vision Workshop (ICCVW) (27 Oct 2019 - 02 Nov 2019 : Seoul, South Korea)|
|Bo Chen, Jiewei Cao, Alvaro Parra and Tat-Jun Chin|
|Abstract:||We propose an approach to estimate the 6DOF pose of a satellite, relative to a canonical pose, from a single image. Such a problem is crucial in many space proximity operations, such as docking, debris removal, and inter-spacecraft communications. Our approach combines machine learning and geometric optimisation, by predicting the coordinates of a set of landmarks in the input image, associating the landmarks to their corresponding 3D points on an a priori reconstructed 3D model, then solving for the object pose using non-linear optimisation. Our approach is not only novel for this specific pose estimation task, which helps to further open up a relatively new domain for machine learning and computer vision, but it also demonstrates superior accuracy and won the first place in the recent Kelvins Pose Estimation Challenge organised by the European Space Agency (ESA).|
|Keywords:||Pose estimation; satellites; three-dimensional displays; training; two dimensional displays; mathematical model; space vehicles|
|Rights:||Copyright © 2019 by The Institute of Electrical and Electronics Engineers, Inc.|
|Appears in Collections:||Computer Vision publications|
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