Iterated SLSJF: A sparse local submap joining algorithm with improved consistency

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dc.contributor.author Huang, S
dc.contributor.author Wang, Z
dc.contributor.author Dissanayake, G
dc.contributor.author Frese, U
dc.date.accessioned 2010-05-28T10:01:06Z
dc.date.issued 2008
dc.identifier.citation Proceedings of the 2008 Australasian Conference on Robotics and Automation, ACRA 2008, 2008
dc.identifier.isbn 9780646506432
dc.identifier.other E1 en_US
dc.identifier.uri http://hdl.handle.net/10453/10995
dc.description.abstract This paper presents a new local submap joining algorithm for building large-scale feature based maps. The algorithm is based on the recently developed Sparse Local Submap Joining Filter (SLSJF) and uses multiple iterations to improve the estimate and hence is called Iterated SLSJF (I-SLSJF). The input to the I-SLSJF algorithm is a sequence of local submaps. The output of the algorithm 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. In the submap joining step of I-SLSJF, whenever the change of state estimate computed by an Extended Information Filter (EIF) is larger than a predefined threshold, the information vector and the information matrix is recomputed as a sum of all the local map contributions. This improves the accuracy of the estimate as well as avoids the possibility that the Jacobian with respect to the same feature gets evaluated at different estimate values, which is one of the major causes of inconsistency for EIF/EKF algorithms. Although the computational cost of I-SLSJF is higher than that of SLSJF, the algorithm can still be implemented effciently due to the exactly sparseness of the information matrix. The new algorithm is compared with EKF SLAM and SLSJF using both computer simulation and experimental examples.
dc.relation.hasversion Accepted manuscript version en_US
dc.title Iterated SLSJF: A sparse local submap joining algorithm with improved consistency
dc.type Conference Proceeding
dc.parent Proceedings of the 2008 Australasian Conference on Robotics and Automation, ACRA 2008
dc.journal.number en_US
dc.publocation Canberra, Australia en_US
dc.identifier.startpage 1 en_US
dc.identifier.endpage 9 en_US
dc.cauo.name FEIT.School of Elec, Mech and Mechatronic Systems en_US
dc.conference Verified OK en_US
dc.conference Australasian Conference on Robotics and Automation
dc.for 0801 Artificial Intelligence and Image Processing
dc.personcode 011224
dc.personcode 040006
dc.personcode 101889
dc.percentage 100 en_US
dc.classification.name Artificial Intelligence and Image Processing en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom Australasian Conference on Robotics and Automation en_US
dc.date.activity 20081203 en_US
dc.date.activity 2008-12-03
dc.location.activity Canberra, Australia en_US
dc.description.keywords en_US
pubs.embargo.period Not known
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 Elec, Mech and Mechatronic Systems
utslib.copyright.status Open Access
utslib.copyright.date 2015-04-15 12:23:47.074767+10
utslib.collection.history General (ID: 2)


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