Tradeoffs in SLAM with sparse information filters

Wang, Z; Huang, S; Dissanayake, G

Tradeoffs in SLAM with sparse information filters

Wang, Z Huang, S

Dissanayake, G

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Publication Type:: Chapter
Citation:: 2008, 42 pp. 339 - 348
Issue Date:: 2008-08-18

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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	2008-08-18	en_US
dc.identifier.citation	2008, 42 pp. 339 - 348	en_US
dc.identifier.isbn	9783540754039	en_US
dc.identifier.uri	http://hdl.handle.net/10453/11649
dc.description.abstract	Designing filters exploiting the sparseness of the information matrix for efficiently solving the simultaneous localization and mapping (SLAM) problem has attracted significant attention during the recent past. The main contribution of this paper is a review of the various sparse information filters proposed in the literature to date, in particular, the compromises used to achieve sparseness. Two of the most recent algorithms that the authors have implemented, Exactly Sparse Extended Information Filter (ESEIF) by Walter et al. [5] and the D-SLAM by Wang et al. [6] are discussed and analyzed in detail. It is proposed that this analysis can stimulate developing a framework suitable for evaluating the relative merits of SLAM algorithms. © 2008 Springer-Verlag Berlin Heidelberg.	en_US
dc.relation.isbasedon	10.1007/978-3-540-75404-6_32	en_US
dc.subject.classification	Industrial Engineering & Automation	en_US
dc.title	Tradeoffs in SLAM with sparse information filters	en_US
dc.type	Chapter
utslib.citation.volume	42	en_US
utslib.for	091007 Manufacturing Robotics and Mechatronics (excl. Automotive Mechatronics)	en_US
utslib.for	090602 Control Systems, Robotics and Automation	en_US
utslib.for	091303 Autonomous Vehicles	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.publication-status	Published	en_US
pubs.volume	42	en_US

Abstract:

Designing filters exploiting the sparseness of the information matrix for efficiently solving the simultaneous localization and mapping (SLAM) problem has attracted significant attention during the recent past. The main contribution of this paper is a review of the various sparse information filters proposed in the literature to date, in particular, the compromises used to achieve sparseness. Two of the most recent algorithms that the authors have implemented, Exactly Sparse Extended Information Filter (ESEIF) by Walter et al. [5] and the D-SLAM by Wang et al. [6] are discussed and analyzed in detail. It is proposed that this analysis can stimulate developing a framework suitable for evaluating the relative merits of SLAM algorithms. © 2008 Springer-Verlag Berlin Heidelberg.

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