Multi-robot Active SLAM based on Submap-joining for Feature-based Representation Environments

Chen, S; Zhao, L; Huang, S; Hao, Q

Multi-robot Active SLAM based on Submap-joining for Feature-based Representation Environments

Chen, S Zhao, L

Huang, S

Hao, Q

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Publication Type:: Conference Proceeding
Citation:: Australasian Conference on Robotics and Automation, ACRA, 2022, 2022-December
Issue Date:: 2022-12-06

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Field	Value	Language
dc.contributor.author	Chen, S
dc.contributor.author	Zhao, L https://orcid.org/0000-0003-4063-8183
dc.contributor.author	Huang, S https://orcid.org/0000-0002-6124-4178
dc.contributor.author	Hao, Q
dc.date	2022-12-06
dc.date.accessioned	2023-04-20T22:09:34Z
dc.date.available	2023-04-20T22:09:34Z
dc.date.issued	2022-12-06
dc.identifier.citation	Australasian Conference on Robotics and Automation, ACRA, 2022, 2022-December
dc.identifier.isbn	9781713866480
dc.identifier.issn	1448-2053
dc.identifier.uri	http://hdl.handle.net/10453/170038
dc.description.abstract	The ability to acquire knowledge of the environment actively is essential for autonomous system. In this paper, we propose a multi-robot active simultaneous localization and mapping (SLAM) algorithm based on mutual information for feature-based representation environments that do not depend on the grid map. A multi-layer motion planner and virtual landmarks are introduced to improve exploration efficiency and reduce planning time. To improve the system's accuracy and scalability, we also developed a decentralized version of the active SLAM based on the submap-joining approach. Both simulations and real-world experiments are performed to validate the effectiveness of the proposed methods.
dc.language	en
dc.relation	Southern University of Science and Technology
dc.relation	http://purl.org/au-research/grants/arc/DP200100982
dc.relation.ispartof	Australasian Conference on Robotics and Automation, ACRA
dc.relation.ispartof	Australasian Conference on Robotics and Automation
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Multi-robot Active SLAM based on Submap-joining for Feature-based Representation Environments
dc.type	Conference Proceeding
utslib.citation.volume	2022-December
utslib.location.activity	Brisbane
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 - RI - Robotics Institute
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	2023-04-20T22:09:32Z
pubs.finish-date	2022-12-08
pubs.publication-status	Published
pubs.start-date	2022-12-06
pubs.volume	2022-December

Abstract:

The ability to acquire knowledge of the environment actively is essential for autonomous system. In this paper, we propose a multi-robot active simultaneous localization and mapping (SLAM) algorithm based on mutual information for feature-based representation environments that do not depend on the grid map. A multi-layer motion planner and virtual landmarks are introduced to improve exploration efficiency and reduce planning time. To improve the system's accuracy and scalability, we also developed a decentralized version of the active SLAM based on the submap-joining approach. Both simulations and real-world experiments are performed to validate the effectiveness of the proposed methods.

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http://hdl.handle.net/10453/170038