Tradeoffs in SLAM with 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 Christian Laugier, Roland Siegwart en_US 2010-06-16T04:55:35Z 2010-06-16T04:55:35Z 2008 en_US
dc.identifier 2008002420 en_US
dc.identifier.citation Wang Zhan, Huang Shoudong, and Dissanayake Gamini 2008, 'Tradeoffs in SLAM with sparse information filters', in (ed.), Springer-Verlag, Berlin Heidelberg, pp. 339-348. en_US
dc.identifier.issn 978-3-540-75403-9 en_US
dc.identifier.other B1 en_US
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. en_US
dc.language en_US
dc.publisher Springer en_US
dc.relation.hasversion Accepted manuscript version en_US
dc.relation.isbasedon en_US
dc.rights The original publication is available at en_US
dc.title Tradeoffs in SLAM with sparse information filters en_US
dc.parent Field and Service Robotics en_US
dc.journal.volume en_US
dc.journal.number en_US
dc.publocation Berlin Heidelberg en_US
dc.identifier.startpage 339 en_US
dc.identifier.endpage 348 en_US FEIT.School of Elec, Mech and Mechatronic Systems en_US
dc.conference Verified OK en_US
dc.for 091303 en_US
dc.personcode 101889 en_US
dc.personcode 040006 en_US
dc.personcode 011224 en_US
dc.percentage 50 en_US Autonomous Vehicles 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 en_US
dc.staffid en_US
dc.staffid 011224 en_US

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