Online estimation of ocean current from sparse GPS data for underwater vehicles

Lee, KMB; Yoo, C; Hollings, B; Anstee, S; Huang, S; Fitch, R

Online estimation of ocean current from sparse GPS data for underwater vehicles

Lee, KMB

Yoo, C

Hollings, B Anstee, S Huang, S

Fitch, R

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Publication Type:: Conference Proceeding
Citation:: Proceedings - IEEE International Conference on Robotics and Automation, 2019, 2019-May pp. 3443 - 3449
Issue Date:: 2019-05-01

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Field	Value	Language
dc.contributor.author	Lee, KMB https://orcid.org/0000-0003-1449-2125	en_US
dc.contributor.author	Yoo, C https://orcid.org/0000-0002-2773-0527	en_US
dc.contributor.author	Hollings, B	en_US
dc.contributor.author	Anstee, S	en_US
dc.contributor.author	Huang, S https://orcid.org/0000-0002-6124-4178	en_US
dc.contributor.author	Fitch, R https://orcid.org/0000-0003-2215-5188	en_US
dc.date.available	2021-05-18T19:01:21Z
dc.date.issued	2019-05-01	en_US
dc.identifier.citation	Proceedings - IEEE International Conference on Robotics and Automation, 2019, 2019-May pp. 3443 - 3449	en_US
dc.identifier.isbn	9781538660263	en_US
dc.identifier.issn	1050-4729	en_US
dc.identifier.uri	http://hdl.handle.net/10453/136468
dc.description.abstract	© 2019 IEEE. Underwater robots are subject to position drift due to the effect of ocean currents and the lack of accurate localisation while submerged. We are interested in exploiting such position drift to estimate the ocean current in the surrounding area, thereby assisting navigation and planning. We present a Gaussian process (GP)-based expectation-maximisation (EM) algorithm that estimates the underlying ocean current using sparse GPS data obtained on the surface and dead-reckoned position estimates. We first develop a specialised GP regression scheme that exploits the incompressibility of ocean currents to counteract the underdetermined nature of the problem. We then use the proposed regression scheme in an EM algorithm that estimates the best-fitting ocean current in between each GPS fix. The proposed algorithm is validated in simulation and on a real dataset, and is shown to be capable of reconstructing the underlying ocean current field. We expect to use this algorithm to close the loop between planning and estimation for underwater navigation in unknown ocean currents.	en_US
dc.relation.ispartof	Proceedings - IEEE International Conference on Robotics and Automation	en_US
dc.relation.isbasedon	10.1109/ICRA.2019.8794308	en_US
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.title	Online estimation of ocean current from sparse GPS data for underwater vehicles	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2019-May	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US
pubs.volume	2019-May	en_US

Abstract:

© 2019 IEEE. Underwater robots are subject to position drift due to the effect of ocean currents and the lack of accurate localisation while submerged. We are interested in exploiting such position drift to estimate the ocean current in the surrounding area, thereby assisting navigation and planning. We present a Gaussian process (GP)-based expectation-maximisation (EM) algorithm that estimates the underlying ocean current using sparse GPS data obtained on the surface and dead-reckoned position estimates. We first develop a specialised GP regression scheme that exploits the incompressibility of ocean currents to counteract the underdetermined nature of the problem. We then use the proposed regression scheme in an EM algorithm that estimates the best-fitting ocean current in between each GPS fix. The proposed algorithm is validated in simulation and on a real dataset, and is shown to be capable of reconstructing the underlying ocean current field. We expect to use this algorithm to close the loop between planning and estimation for underwater navigation in unknown ocean currents.

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http://hdl.handle.net/10453/136468