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dc.contributor.authorWalcott-Bryant, Aisha
dc.contributor.authorKaess, Michael
dc.contributor.authorJohannsson, Hordur
dc.contributor.authorLeonard, John Joseph
dc.date.accessioned2013-05-16T18:39:44Z
dc.date.available2013-05-16T18:39:44Z
dc.date.issued2013-05-16
dc.date.submitted2012-10
dc.identifier.isbn978-1-4673-1737-5
dc.identifier.issn2153-0858
dc.identifier.urihttp://hdl.handle.net/1721.1/78911
dc.description.abstractMaintaining a map of an environment that changes over time is a critical challenge in the development of persistently autonomous mobile robots. Many previous approaches to mapping assume a static world. In this work we incorporate the time dimension into the mapping process to enable a robot to maintain an accurate map while operating in dynamical environments. This paper presents Dynamic Pose Graph SLAM (DPG-SLAM), an algorithm designed to enable a robot to remain localized in an environment that changes substantially over time. Using incremental smoothing and mapping (iSAM) as the underlying SLAM state estimation engine, the Dynamic Pose Graph evolves over time as the robot explores new places and revisits previously mapped areas. The approach has been implemented for planar indoor environments, using laser scan matching to derive constraints for SLAM state estimation. Laser scans for the same portion of the environment at different times are compared to perform change detection; when sufficient change has occurred in a location, the dynamic pose graph is edited to remove old poses and scans that no longer match the current state of the world. Experimental results are shown for two real-world dynamic indoor laser data sets, demonstrating the ability to maintain an up-to-date map despite long-term environmental changes.en_US
dc.description.sponsorshipUnited States. National Oceanic and Atmospheric Administration (Grant NA06OAR4170019)en_US
dc.description.sponsorshipUnited States. Office of Naval Research (Grant N00014-10-1-0936)en_US
dc.description.sponsorshipUnited States. Office of Naval Research (Grant N00014-12-10020)en_US
dc.language.isoen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/IROS.2012.6385561en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alike 3.0en_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/en_US
dc.sourceMIT web domainen_US
dc.titleDynamic pose graph SLAM: Long-term mapping in low dynamic environmentsen_US
dc.typeArticleen_US
dc.identifier.citationWalcott-Bryant, Aisha et al. “Dynamic pose graph SLAM: Long-term mapping in low dynamic environments.” Proceedings of the 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2012): 1871–1878.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mechanical Engineeringen_US
dc.contributor.mitauthorWalcott-Bryant, Aisha
dc.contributor.mitauthorKaess, Michael
dc.contributor.mitauthorJohannsson, Hordur
dc.contributor.mitauthorLeonard, John Joseph
dc.relation.journalProceedings of the 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)en_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
dspace.orderedauthorsWalcott-Bryant, Aisha; Kaess, Michael; Johannsson, Hordur; Leonard, John J.en
dc.identifier.orcidhttps://orcid.org/0000-0002-8863-6550
mit.licenseOPEN_ACCESS_POLICYen_US
mit.metadata.statusComplete


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