3D I-SLSJF: A consistent sparse local submap joining algorithm for building large-scale 3D map

Hu, G; Huang, S; Dissanayake, G

3D I-SLSJF: A consistent sparse local submap joining algorithm for building large-scale 3D map

Hu, G Huang, S

Dissanayake, G

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the IEEE Conference on Decision and Control, 2009, pp. 6040 - 6045
Issue Date:: 2009-12-01

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Field	Value	Language
dc.contributor.author	Hu, G	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	2009-12-01	en_US
dc.identifier.citation	Proceedings of the IEEE Conference on Decision and Control, 2009, pp. 6040 - 6045	en_US
dc.identifier.isbn	9781424438716	en_US
dc.identifier.issn	0191-2216	en_US
dc.identifier.uri	http://hdl.handle.net/10453/10822
dc.description.abstract	This paper presents an efficient and reliable algorithm for autonomous robots to build large-scale three dimensional maps by combining small local submaps. The algorithm is a generalization of our recent work on two dimensional map joining algorithm - Iterated Sparse Local Submap Joining Filter (I-SLSJF). The 3D local submap joining problem is formulated as a least squares optimization problem and solved by Extended Information Filter (EIF) together with smoothing and iterations. The resulting information matrix is exactly sparse and this makes the algorithm efficient. The smoothing and iteration steps improve the accuracy and consistency of the estimate. The consistency and efficiency of 3D I-SLSJF is demonstrated by comparing the algorithm with some existing algorithms using computer simulations. ©2009 IEEE.	en_US
dc.relation.ispartof	Proceedings of the IEEE Conference on Decision and Control	en_US
dc.relation.isbasedon	10.1109/CDC.2009.5399747	en_US
dc.title	3D I-SLSJF: A consistent sparse local submap joining algorithm for building large-scale 3D map	en_US
dc.type	Conference Proceeding
utslib.for	080605 Decision Support and Group Support Systems	en_US
dc.location.activity	Shanghai, China	en_US
dc.location.activity	Shanghai, 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 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 an efficient and reliable algorithm for autonomous robots to build large-scale three dimensional maps by combining small local submaps. The algorithm is a generalization of our recent work on two dimensional map joining algorithm - Iterated Sparse Local Submap Joining Filter (I-SLSJF). The 3D local submap joining problem is formulated as a least squares optimization problem and solved by Extended Information Filter (EIF) together with smoothing and iterations. The resulting information matrix is exactly sparse and this makes the algorithm efficient. The smoothing and iteration steps improve the accuracy and consistency of the estimate. The consistency and efficiency of 3D I-SLSJF is demonstrated by comparing the algorithm with some existing algorithms using computer simulations. ©2009 IEEE.

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