Multi-robot Feature-based SLAM using Submap Joining

Chen, S; Han, R; Zhao, L; Hang, S; Hao, Q

Multi-robot Feature-based SLAM using Submap Joining

Chen, S Han, R Zhao, L

Hang, S Hao, Q

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Publisher:: ARAA
Publication Type:: Conference Proceeding
Citation:: Australasian Conference on Robotics and Automation, ACRA Online, 2021, 2021-December, pp. 1-8
Issue Date:: 2021-01-01

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Field	Value	Language
dc.contributor.author	Chen, S
dc.contributor.author	Han, R
dc.contributor.author	Zhao, L https://orcid.org/0000-0003-4063-8183
dc.contributor.author	Hang, S
dc.contributor.author	Hao, Q
dc.date	2021-12-06
dc.date.accessioned	2022-07-03T21:57:27Z
dc.date.available	2022-07-03T21:57:27Z
dc.date.issued	2021-01-01
dc.identifier.citation	Australasian Conference on Robotics and Automation, ACRA Online, 2021, 2021-December, pp. 1-8
dc.identifier.isbn	9781713841487
dc.identifier.issn	1448-2053
dc.identifier.uri	http://hdl.handle.net/10453/158575
dc.description.abstract	This paper considers the feature-based SLAM using multiple robots. To reduce the computational complexity and data storage, a distributed multi-robot feature-based SLAM algorithm under submap joining scheme is proposed. Each robot first independently builds a submap using the information collected by its sensors. Once the robots can observe each other, the submaps can then be fused together to obtain a global map. We implemented and tested the proposed algorithm in both simulation and real world environments. Both simulation and experimental results have validated the robustness and accuracy of the proposed algorithm.
dc.language	en
dc.publisher	ARAA
dc.relation.ispartof	Australasian Conference on Robotics and Automation, ACRA Online
dc.relation.ispartof	Australasian Conference on Robotics and Automation
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Multi-robot Feature-based SLAM using Submap Joining
dc.type	Conference Proceeding
utslib.citation.volume	2021-December
utslib.location.activity	Melbourne, Australia
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/Strength - CAS - Centre for Autonomous Systems
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.date.updated	2022-07-03T21:57:25Z
pubs.finish-date	2021-12-08
pubs.place-of-publication	Australia
pubs.publication-status	Published
pubs.start-date	2021-12-06
pubs.volume	2021-December
dc.location	Australia

Abstract:

This paper considers the feature-based SLAM using multiple robots. To reduce the computational complexity and data storage, a distributed multi-robot feature-based SLAM algorithm under submap joining scheme is proposed. Each robot first independently builds a submap using the information collected by its sensors. Once the robots can observe each other, the submaps can then be fused together to obtain a global map. We implemented and tested the proposed algorithm in both simulation and real world environments. Both simulation and experimental results have validated the robustness and accuracy of the proposed algorithm.

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http://hdl.handle.net/10453/158575