A new state vector and a map joining algorithm for range-only SLAM

Ahmad, A; Huang, S; Wang, JJ; Dissanayake, G

A new state vector and a map joining algorithm for range-only SLAM

Ahmad, A Huang, S

Wang, JJ

Dissanayake, G

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Publication Type:: Conference Proceeding
Citation:: 2012 12th International Conference on Control, Automation, Robotics and Vision, ICARCV 2012, 2012, pp. 1024 - 1029
Issue Date:: 2012-12-01

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Field	Value	Language
dc.contributor.author	Ahmad, A	en_US
dc.contributor.author	Huang, S https://orcid.org/0000-0002-6124-4178	en_US
dc.contributor.author	Wang, JJ https://orcid.org/0000-0002-6282-0456	en_US
dc.contributor.author	Dissanayake, G https://orcid.org/0000-0002-7992-0680	en_US
dc.date.issued	2012-12-01	en_US
dc.identifier.citation	2012 12th International Conference on Control, Automation, Robotics and Vision, ICARCV 2012, 2012, pp. 1024 - 1029	en_US
dc.identifier.isbn	9781467318716	en_US
dc.identifier.uri	http://hdl.handle.net/10453/32347
dc.description.abstract	This paper presents a new state vector and a map joining algorithm for range-only SLAM problems. Local maps are built by least squares optimization using the new state vector and a landmark initialization strategy which is an improvement on our preliminary work [1]. The map joining algorithm combines the local maps using least squares optimization to maintain the estimation consistency. Both the local map building and the map joining algorithm maintain a list of 'unused range observations' to minimize the potential for information loss. The accuracy of the proposed method is evaluated using a simulation dataset, and an experimental dataset provided by the Robotics Institute at Carnegie Mellon University (CMU). © 2012 IEEE.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP120102786
dc.relation.ispartof	2012 12th International Conference on Control, Automation, Robotics and Vision, ICARCV 2012	en_US
dc.relation.isbasedon	10.1109/ICARCV.2012.6485298	en_US
dc.title	A new state vector and a map joining algorithm for range-only SLAM	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Guangzhou, PEOPLES R CHINA
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 Civil and Environmental Engineering
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
pubs.organisational-group	/University of Technology Sydney/Strength - CBI - Centre for Built Infrastructure
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

This paper presents a new state vector and a map joining algorithm for range-only SLAM problems. Local maps are built by least squares optimization using the new state vector and a landmark initialization strategy which is an improvement on our preliminary work [1]. The map joining algorithm combines the local maps using least squares optimization to maintain the estimation consistency. Both the local map building and the map joining algorithm maintain a list of 'unused range observations' to minimize the potential for information loss. The accuracy of the proposed method is evaluated using a simulation dataset, and an experimental dataset provided by the Robotics Institute at Carnegie Mellon University (CMU). © 2012 IEEE.

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http://hdl.handle.net/10453/32347