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dc.contributor.authorDeray, Jeremie
dc.contributor.authorSolà Ortega, Joan
dc.contributor.authorAndrade-Cetto, Juan
dc.contributor.otherInstitut de Robòtica i Informàtica Industrial
dc.date.accessioned2017-11-20T15:39:15Z
dc.date.available2017-11-20T15:39:15Z
dc.date.issued2017
dc.identifier.citationDeray, J., Solá, J., Andrade-Cetto, J. Word ordering and document adjacency for large loop closure detection in 2D laser maps. "IEEE robotics and automation letters", 2017, vol. 2, núm. 3, p. 1532-1539.
dc.identifier.issn2377-3766
dc.identifier.urihttp://hdl.handle.net/2117/110932
dc.description© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
dc.description.abstractWe address in this paper the problem of loop closure detection for laser-based simultaneous localization and mapping (SLAM) of very large areas. Consistent with the state of the art, the map is encoded as a graph of poses, and to cope with very large mapping capabilities, loop closures are asserted by comparing the features extracted from a query laser scan against a previously acquired corpus of scan features using a bag-ofwords (BoW) scheme. Two contributions are here presented. First, to benefit from the graph topology, feature frequency scores in the BoW are computed not only for each individual scan but also from neighboring scans in the SLAM graph. This has the effect of enforcing neighbor relational information during document matching. Secondly, a weak geometric check that takes into account feature ordering and occlusions is introduced that substantially improves loop closure detection performance. The two contributions are evaluated both separately and jointly on four common SLAM datasets, and are shown to improve the state-of-the-art performance both in terms of precision and recall in most of the cases. Moreover, our current implementation is designed to work at nearly frame rate, allowing loop closure query resolution at nearly 22 Hz for the best case scenario and 2 Hz for the worst case scenario.
dc.format.extent8 p.
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Informàtica::Robòtica
dc.subject.otherrobots
dc.titleWord ordering and document adjacency for large loop closure detection in 2D laser maps
dc.typeArticle
dc.contributor.groupUniversitat Politècnica de Catalunya. VIS - Visió Artificial i Sistemes Intel·ligents
dc.identifier.doi10.1109/LRA.2017.2657796
dc.description.peerreviewedPeer Reviewed
dc.subject.inspecClassificació INSPEC::Automation::Robots
dc.relation.publisherversionhttp://ieeexplore.ieee.org/document/7833064/
dc.rights.accessOpen Access
local.identifier.drac21561010
dc.description.versionPostprint (author's final draft)
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/H2020/687534/EU/Tight integration of EGNSS and on-board sensors for port vehicle automation/LOGIMATIC
local.citation.authorDeray, J.; Solá, J.; Andrade-Cetto, J.
local.citation.publicationNameIEEE robotics and automation letters
local.citation.volume2
local.citation.number3
local.citation.startingPage1532
local.citation.endingPage1539


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