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Compressed Unscented Kalman Filter-Based SLAM

Cheng, Jiantong; Kim, Jonghyuk; Jiang, Zhenyu; Yang, Xixiang

Description

This paper proposes a real-time nonlinear filtering approach for the SLAM problem, termed as compressed Unscented Kalman filter (CUKF). A partial sampling strategy was recently proposed to make the computational complexity of the UKF quadratic with the state-vector dimension. However, the quadratic complexity remains intractable for the large-scale SLAM. To address this problem, we firstly prove the equivalence of the partial and full sampling strategies for the decoupled nonlinear system. Then...[Show more]

dc.contributor.authorCheng, Jiantong
dc.contributor.authorKim, Jonghyuk
dc.contributor.authorJiang, Zhenyu
dc.contributor.authorYang, Xixiang
dc.coverage.spatialBali, Indonesia
dc.date.accessioned2015-12-08T22:46:20Z
dc.date.createdDecember 5-10 2014
dc.identifier.urihttp://hdl.handle.net/1885/38099
dc.description.abstractThis paper proposes a real-time nonlinear filtering approach for the SLAM problem, termed as compressed Unscented Kalman filter (CUKF). A partial sampling strategy was recently proposed to make the computational complexity of the UKF quadratic with the state-vector dimension. However, the quadratic complexity remains intractable for the large-scale SLAM. To address this problem, we firstly prove the equivalence of the partial and full sampling strategies for the decoupled nonlinear system. Then a compressed form is presented by reformulating the cross-correlation items. Finally, experimental results based on simulated and practical datasets validate the effectiveness of the proposed approach.
dc.publisherIEEE
dc.relation.ispartofseriesIEEE International Conference on Robotics and Biomimetics 2014
dc.sourceProceedings of the IEEE International Conference on Robotics and Biomimetics
dc.titleCompressed Unscented Kalman Filter-Based SLAM
dc.typeConference paper
local.description.notesImported from ARIES
dc.date.issued2014
local.identifier.absfor080101 - Adaptive Agents and Intelligent Robotics
local.identifier.ariespublicationU5431022xPUB157
local.type.statusPublished Version
local.contributor.affiliationCheng, Jiantong, National University of Defense Technology
local.contributor.affiliationKim, Jonghyuk, College of Engineering and Computer Science, ANU
local.contributor.affiliationJiang, Zhenyu, National University of Defense Technology
local.contributor.affiliationYang, Xixiang, College of Aerospace Science and Engineering, National University of Defense Technology
local.description.embargo2037-12-31
local.bibliographicCitation.startpage1602
local.bibliographicCitation.lastpage1607
local.identifier.doi10.1109/ROBIO.2014.7090563
local.identifier.absseo861399 - Transport Equipment not elsewhere classified
dc.date.updated2015-12-08T11:00:33Z
local.identifier.scopusID2-s2.0-84929584685
CollectionsANU Research Publications

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