Sparse local submap joining filter for building large-scale maps

Huang, S; Wang, Z; Dissanayake, G

Sparse local submap joining filter for building large-scale maps

Huang, S

Wang, Z Dissanayake, G

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Robotics, 2008, 24 (5), pp. 1121 - 1130
Issue Date:: 2008-09-18

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Field	Value	Language
dc.contributor.author	Huang, S https://orcid.org/0000-0002-6124-4178	en_US
dc.contributor.author	Wang, Z	en_US
dc.contributor.author	Dissanayake, G https://orcid.org/0000-0002-7992-0680	en_US
dc.date.issued	2008-09-18	en_US
dc.identifier.citation	IEEE Transactions on Robotics, 2008, 24 (5), pp. 1121 - 1130	en_US
dc.identifier.issn	1552-3098	en_US
dc.identifier.uri	http://hdl.handle.net/10453/9354
dc.description.abstract	This paper presents a novel local submap joining algorithm for building large-scale feature-based maps: sparse local submap joining filter (SLSJF). The input to the filter is a sequence of local submaps. Each local submap is represented in a coordinate frame defined by the robot pose at which the map is initiated. The local submap state vector consists of the positions of all the local features and the final robot pose within the submap. The output of the filter is a global map containing the global positions of all the features as well as all the robot start/end poses of the local submaps. Use of an extended information filter (EIF) for fusing submaps makes the information matrix associated with SLSJF exactly sparse. The sparse structure together with a novel state vector and covariance submatrix recovery technique makes the SLSJF computationally very efficient. The SLSJF is a canonical and efficient submap joining solution for large-scale simultaneous localization and mapping (SLAM) problems that makes use of consistent local submaps generated by any reliable SLAM algorithm. The effectiveness and efficiency of the new algorithm is verified through computer simulations and experiments. © 2008 IEEE.	en_US
dc.relation.ispartof	IEEE Transactions on Robotics	en_US
dc.relation.isbasedon	10.1109/TRO.2008.2003259	en_US
dc.subject.classification	Industrial Engineering & Automation	en_US
dc.title	Sparse local submap joining filter for building large-scale maps	en_US
dc.type	Journal Article
utslib.citation.volume	5	en_US
utslib.citation.volume	24	en_US
utslib.for	091303 Autonomous Vehicles	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0913 Mechanical Engineering	en_US
dc.location.activity	ISI:000260865400018	en_US
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.issue	5	en_US
pubs.publication-status	Published	en_US
pubs.volume	24	en_US

Abstract:

This paper presents a novel local submap joining algorithm for building large-scale feature-based maps: sparse local submap joining filter (SLSJF). The input to the filter is a sequence of local submaps. Each local submap is represented in a coordinate frame defined by the robot pose at which the map is initiated. The local submap state vector consists of the positions of all the local features and the final robot pose within the submap. The output of the filter is a global map containing the global positions of all the features as well as all the robot start/end poses of the local submaps. Use of an extended information filter (EIF) for fusing submaps makes the information matrix associated with SLSJF exactly sparse. The sparse structure together with a novel state vector and covariance submatrix recovery technique makes the SLSJF computationally very efficient. The SLSJF is a canonical and efficient submap joining solution for large-scale simultaneous localization and mapping (SLAM) problems that makes use of consistent local submaps generated by any reliable SLAM algorithm. The effectiveness and efficiency of the new algorithm is verified through computer simulations and experiments. © 2008 IEEE.

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http://hdl.handle.net/10453/9354