Simultaneous Localization and Mapping: Exactly Sparse Information Filters

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Show simple item record Wang, Zhan en_US Huang, Shoudong en_US Dissanayake, Gamini en_US
dc.contributor.editor en_US 2012-10-12T03:30:40Z 2012-10-12T03:30:40Z 2011 en_US
dc.identifier 2010006665 en_US
dc.identifier.citation Wang Zhan, Huang Shoudong and Dissanayake Gamini 2011, 'Simultaneous Localization and Mapping: Exactly Sparse Information Filters',World Scientific, Singapore en_US
dc.identifier.issn 981-4350-31-1 en_US
dc.identifier.other A1 en_US
dc.description.abstract Simultaneous localization and mapping (SLAM) is a process where an autonomous vehicle builds a map of an unknown environment while concurrently generating an estimate for its location. This book is concerned with computationally efficient solutions to the large scale SLAM problems using exactly sparse Extended Information Filters (EIF). The invaluable book also provides a comprehensive theoretical analysis of the properties of the information matrix in EIF-based algorithms for SLAM. Three exactly sparse information filters for SLAM are described in detail, together with two efficient and exact methods for recovering the state vector and the covariance matrix. Proposed algorithms are extensively evaluated both in simulation and through experiments. en_US
dc.language en_US
dc.publisher World Scientific en_US
dc.relation.isbasedon NA en_US
dc.title Simultaneous Localization and Mapping: Exactly Sparse Information Filters en_US
dc.parent en_US
dc.journal.volume en_US
dc.journal.number en_US
dc.publocation Singapore en_US
dc.identifier.startpage en_US
dc.identifier.endpage en_US FEIT.School of Elec, Mech and Mechatronic Systems en_US
dc.conference Verified OK en_US
dc.for 091300 en_US
dc.personcode 101889 en_US
dc.personcode 040006 en_US
dc.personcode 011224 en_US
dc.percentage 50 en_US Mechanical Engineering en_US
dc.classification.type FOR-08 en_US
dc.edition 1 en_US
dc.custom en_US en_US
dc.location.activity en_US
dc.description.keywords NA en_US
dc.staffid en_US
dc.staffid 011224 en_US

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