A branch-and-bound based globally optimal solution to 2D multi-robot relative pose estimation problems

Wang, H; Zhao, X; Huang, S; Li, Q; Liu, Y

A branch-and-bound based globally optimal solution to 2D multi-robot relative pose estimation problems

Wang, H Zhao, X Huang, S

Li, Q Liu, Y

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Publisher:: PERGAMON-ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: Automatica, 2024, 164
Issue Date:: 2024-06-01

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Field	Value	Language
dc.contributor.author	Wang, H
dc.contributor.author	Zhao, X
dc.contributor.author	Huang, S https://orcid.org/0000-0002-6124-4178
dc.contributor.author	Li, Q
dc.contributor.author	Liu, Y
dc.date.accessioned	2024-10-09T04:15:05Z
dc.date.available	2024-10-09T04:15:05Z
dc.date.issued	2024-06-01
dc.identifier.citation	Automatica, 2024, 164
dc.identifier.issn	0005-1098
dc.identifier.issn	1873-2836
dc.identifier.uri	http://hdl.handle.net/10453/181350
dc.description.abstract	The relative pose estimation problem is important for multi-robot systems when they execute a variety of tasks cooperatively, such as cooperative sensing, multi-robot search and rescue, formation control. The finding of reliable solutions to this problem is essential, however, algorithms that guarantee to provide globally optimal solutions are still in demand. In this paper, an alternative dimensionality reduced objective function is proposed for the planar ground multi-robot relative pose estimation problem, and a new branch-and-bound (BnB) based method is developed, through which initialization is not required and the globally optimal solution is guaranteed under arbitrary noise levels. We demonstrate that the globally optimal solutions obtained via the algorithm proposed in this paper are superior than that obtained by existing classic methods such as SE-Sync algorithm when noise levels are high. Moreover, the method proposed is simple and easy to implement.
dc.language	English
dc.publisher	PERGAMON-ELSEVIER SCIENCE LTD
dc.relation.ispartof	Automatica
dc.relation.isbasedon	10.1016/j.automatica.2024.111654
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	01 Mathematical Sciences, 08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Industrial Engineering & Automation
dc.subject.classification	40 Engineering
dc.subject.classification	46 Information and computing sciences
dc.subject.classification	49 Mathematical sciences
dc.title	A branch-and-bound based globally optimal solution to 2D multi-robot relative pose estimation problems
dc.type	Journal Article
utslib.citation.volume	164
utslib.for	01 Mathematical Sciences
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
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/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Robotics Institute (RI)
utslib.copyright.status	closed_access	*
dc.date.updated	2024-10-09T04:15:03Z
pubs.publication-status	Published
pubs.volume	164

Abstract:

The relative pose estimation problem is important for multi-robot systems when they execute a variety of tasks cooperatively, such as cooperative sensing, multi-robot search and rescue, formation control. The finding of reliable solutions to this problem is essential, however, algorithms that guarantee to provide globally optimal solutions are still in demand. In this paper, an alternative dimensionality reduced objective function is proposed for the planar ground multi-robot relative pose estimation problem, and a new branch-and-bound (BnB) based method is developed, through which initialization is not required and the globally optimal solution is guaranteed under arbitrary noise levels. We demonstrate that the globally optimal solutions obtained via the algorithm proposed in this paper are superior than that obtained by existing classic methods such as SE-Sync algorithm when noise levels are high. Moreover, the method proposed is simple and easy to implement.

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http://hdl.handle.net/10453/181350