Accurate large-scale bearing-only SLAM by map joining

Ahmad, A; Huang, S; Dissanayake, G

Accurate large-scale bearing-only SLAM by map joining

Ahmad, A Huang, S

Dissanayake, G

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2010 Australasian Conference on Robotics and Automation, ACRA 2010, 2010
Issue Date:: 2010-12-01

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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	Dissanayake, G https://orcid.org/0000-0002-7992-0680	en_US
dc.date.issued	2010-12-01	en_US
dc.identifier.citation	Proceedings of the 2010 Australasian Conference on Robotics and Automation, ACRA 2010, 2010	en_US
dc.identifier.isbn	9780980740417	en_US
dc.identifier.uri	http://hdl.handle.net/10453/16477
dc.description.abstract	This paper presents a bearing-only SLAM algorithm that generates accurate and consistent maps of large environments by joining a series of small local maps. The local maps are built by least squares optimization with a proper landmark initialization technique. The local maps are then combined to build global map using Iterated Sparse Local Submap Joining Filter (I-SLSJF). The accuracy and consistency of the proposed algorithm is evaluated using simulation data sets. The algorithm is also tested using the DLR-Spatial-Cognition data set and the preprocessed Victoria Park data where the range information is ignored. The global map results are very similar to the result of full least squares optimization starting with very accurate initial values. As I-SLSJF is able to join a given set of local maps and associated uncertainties efficiently without any information loss, these results demonstrate that focusing on generating accurate local maps is a promising direction for solving large-scale bearing-only SLAM problems.	en_US
dc.relation.ispartof	Proceedings of the 2010 Australasian Conference on Robotics and Automation, ACRA 2010	en_US
dc.title	Accurate large-scale bearing-only SLAM by map joining	en_US
dc.type	Conference Proceeding
utslib.for	090602 Control Systems, Robotics and Automation	en_US
dc.location.activity	Brisbane, Queensland, Australia	en_US
dc.location.activity	Adelaide, Australia
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	open_access
pubs.publication-status	Published	en_US

Abstract:

This paper presents a bearing-only SLAM algorithm that generates accurate and consistent maps of large environments by joining a series of small local maps. The local maps are built by least squares optimization with a proper landmark initialization technique. The local maps are then combined to build global map using Iterated Sparse Local Submap Joining Filter (I-SLSJF). The accuracy and consistency of the proposed algorithm is evaluated using simulation data sets. The algorithm is also tested using the DLR-Spatial-Cognition data set and the preprocessed Victoria Park data where the range information is ignored. The global map results are very similar to the result of full least squares optimization starting with very accurate initial values. As I-SLSJF is able to join a given set of local maps and associated uncertainties efficiently without any information loss, these results demonstrate that focusing on generating accurate local maps is a promising direction for solving large-scale bearing-only SLAM problems.

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http://hdl.handle.net/10453/16477