D-SLAM: Decoupled localization and mapping for autonomous robots

Wang, Z; Huang, S; Dissanayake, G

D-SLAM: Decoupled localization and mapping for autonomous robots

Wang, Z Huang, S

Dissanayake, G

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Publication Type:: Conference Proceeding
Citation:: Springer Tracts in Advanced Robotics, 2007, 28
Issue Date:: 2007-03-29

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Field	Value	Language
dc.contributor.author	Wang, Z	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	2007-03-29	en_US
dc.identifier.citation	Springer Tracts in Advanced Robotics, 2007, 28	en_US
dc.identifier.issn	1610-7438	en_US
dc.identifier.uri	http://hdl.handle.net/10453/1820
dc.description.abstract	The main contribution of this paper is the reformulation of the simultaneous localization and mapping (SLAM) problem for mobile robots such that the mapping and localization can be treated as two concurrent yet separated processes: D-SLAM (decoupled SLAM). It is shown that SLAM can be decoupled into solving a non-linear static estimation problem for mapping and a low-dimensional dynamic estimation problem for localization. The mapping problem can be solved using an Extended Information Filter where the information matrix is shown to be exactly sparse. A significant saving in the computational effort can be achieved for large scale problems by exploiting the special properties of sparse matrices. An important feature of D-SLAM is that the correlation among landmarks are still kept and it is demonstrated that the uncertainty of the map landmarks monotonically decrease. The algorithm is illustrated through computer simulations and experiments.	en_US
dc.relation.ispartof	Springer Tracts in Advanced Robotics	en_US
dc.subject.classification	Industrial Engineering & Automation	en_US
dc.title	D-SLAM: Decoupled localization and mapping for autonomous robots	en_US
dc.type	Conference Proceeding
utslib.citation.volume	28	en_US
utslib.for	091007 Manufacturing Robotics and Mechatronics (excl. Automotive Mechatronics)	en_US
dc.location.activity	San Francisco, USA	en_US
dc.location.activity	San Francisco, CA
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
pubs.volume	28	en_US

Abstract:

The main contribution of this paper is the reformulation of the simultaneous localization and mapping (SLAM) problem for mobile robots such that the mapping and localization can be treated as two concurrent yet separated processes: D-SLAM (decoupled SLAM). It is shown that SLAM can be decoupled into solving a non-linear static estimation problem for mapping and a low-dimensional dynamic estimation problem for localization. The mapping problem can be solved using an Extended Information Filter where the information matrix is shown to be exactly sparse. A significant saving in the computational effort can be achieved for large scale problems by exploiting the special properties of sparse matrices. An important feature of D-SLAM is that the correlation among landmarks are still kept and it is demonstrated that the uncertainty of the map landmarks monotonically decrease. The algorithm is illustrated through computer simulations and experiments.

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http://hdl.handle.net/10453/1820