Iterative Smoothing and Outlier Detection for Underwater Navigation

Hassan, S; Byun, H; Kim, J

Iterative Smoothing and Outlier Detection for Underwater Navigation

Hassan, S Byun, H Kim, J

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Publication Type:: Conference Proceeding
Citation:: Australasian Conference on Robotics and Automation, ACRA, 2021, 2021-December
Issue Date:: 2021-01-01

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Field	Value	Language
dc.contributor.author	Hassan, S
dc.contributor.author	Byun, H
dc.contributor.author	Kim, J
dc.date.accessioned	2023-04-05T05:16:09Z
dc.date.available	2023-04-05T05:16:09Z
dc.date.issued	2021-01-01
dc.identifier.citation	Australasian Conference on Robotics and Automation, ACRA, 2021, 2021-December
dc.identifier.isbn	9781713841487
dc.identifier.issn	1448-2053
dc.identifier.uri	http://hdl.handle.net/10453/169205
dc.description.abstract	Underwater visual-inertial navigation is challenging due to the poor visibility and presence of outliers in underwater environments. The navigation performance is closely related to outlier detection and elimination. Existing methods assume the inertial odometry is accurate enough for outlier detection, which is not valid for low-cost inertial applications. We propose a novel iterative smoothing and outlier detection method aiming for underwater navigation. Using the dataset collected from an underwater robot and fiducial markers, experimental results confirm that the method can successfully eliminate the outliers and enhance navigation accuracy.
dc.language	en
dc.relation.ispartof	Australasian Conference on Robotics and Automation, ACRA
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Iterative Smoothing and Outlier Detection for Underwater Navigation
dc.type	Conference Proceeding
utslib.citation.volume	2021-December
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
utslib.copyright.status	open_access	*
dc.date.updated	2023-04-05T05:16:08Z
pubs.publication-status	Published
pubs.volume	2021-December

Abstract:

Underwater visual-inertial navigation is challenging due to the poor visibility and presence of outliers in underwater environments. The navigation performance is closely related to outlier detection and elimination. Existing methods assume the inertial odometry is accurate enough for outlier detection, which is not valid for low-cost inertial applications. We propose a novel iterative smoothing and outlier detection method aiming for underwater navigation. Using the dataset collected from an underwater robot and fiducial markers, experimental results confirm that the method can successfully eliminate the outliers and enhance navigation accuracy.

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http://hdl.handle.net/10453/169205