Towards multi-vehicle simultaneous localisation and mapping

Williams, SB; Dissanayake, G; Durrant-Whyte, H

Towards multi-vehicle simultaneous localisation and mapping

Williams, SB Dissanayake, G

Durrant-Whyte, H

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Publication Type:: Conference Proceeding
Citation:: Proceedings - IEEE International Conference on Robotics and Automation, 2002, 3 pp. 2743 - 2748
Issue Date:: 2002-01-01

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Field	Value	Language
dc.contributor.author	Williams, SB	en_US
dc.contributor.author	Dissanayake, G https://orcid.org/0000-0002-7992-0680	en_US
dc.contributor.author	Durrant-Whyte, H	en_US
dc.date.issued	2002-01-01	en_US
dc.identifier.citation	Proceedings - IEEE International Conference on Robotics and Automation, 2002, 3 pp. 2743 - 2748	en_US
dc.identifier.issn	1050-4729	en_US
dc.identifier.uri	http://hdl.handle.net/10453/2828
dc.description.abstract	This paper presents a novel approach to the multi-vehicle Simultaneous Localisation and Mapping (SLAM) problem that exploits the manner in which observations are fused into the global map of the environment to manage the computational complexity of the algorithm and improve the data association process. Rather than incorporating every observation directly into the global map of the environment, the Constrained Local Submap Filter (CLSF) relies on creating an independent, local submap of the features in the immediate vicinity of the vehicle. This local submap is then periodically fused into the global map of the environment. This representation has been shown to reduce the computational complexity of maintaining the global map estimates as well as improving the data association process. This paper examines the prospect of applying the CLSF algorithm to the multi-vehicle SLAM problem.	en_US
dc.relation.ispartof	Proceedings - IEEE International Conference on Robotics and Automation	en_US
dc.title	Towards multi-vehicle simultaneous localisation and mapping	en_US
dc.type	Conference Proceeding
utslib.citation.volume	3	en_US
utslib.for	091007 Manufacturing Robotics and Mechatronics (excl. Automotive Mechatronics)	en_US
utslib.for	090602 Control Systems, Robotics and Automation	en_US
utslib.for	091303 Autonomous Vehicles	en_US
dc.location.activity	Washington DC,USA	en_US
dc.location.activity	WASHINGTON, DC
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
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	3	en_US

Abstract:

This paper presents a novel approach to the multi-vehicle Simultaneous Localisation and Mapping (SLAM) problem that exploits the manner in which observations are fused into the global map of the environment to manage the computational complexity of the algorithm and improve the data association process. Rather than incorporating every observation directly into the global map of the environment, the Constrained Local Submap Filter (CLSF) relies on creating an independent, local submap of the features in the immediate vicinity of the vehicle. This local submap is then periodically fused into the global map of the environment. This representation has been shown to reduce the computational complexity of maintaining the global map estimates as well as improving the data association process. This paper examines the prospect of applying the CLSF algorithm to the multi-vehicle SLAM problem.

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http://hdl.handle.net/10453/2828