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dc.contributor.authorLincoln, Luke
dc.contributor.authorGonzalez, Ruben
dc.date.accessioned2018-03-19T01:30:30Z
dc.date.available2018-03-19T01:30:30Z
dc.date.issued2016
dc.identifier.issn2409-0026
dc.identifier.doi10.4108/eai.12-9-2016.151675
dc.identifier.urihttp://hdl.handle.net/10072/101116
dc.description.abstractIn image processing phase correlation has been shown to outperform feature matching in several contexts. In this paper, a novel volume registration technique is proposed for solving the simultaneous localization and mapping (SLAM) problem. Unlike existing methods which rely on iterative feature matching, the proposed method utilises 3D phase correlation. This method provides high noise robustness, even in the presence of moving objects within the scene which are problematic for SLAM systems. Furthermore, a novel projection method is proposed which performs Fourier based volume registration 3 times faster. Quantitative and qualitative experimental results are presented, evaluating the proposed method’s the noise sensitivity, performance, reconstruction quality and robustness in the context of moving objects.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherInstitute for Computer Sciences, Social Informatics and Telecommunications Engineering (I C S T)
dc.relation.ispartofpagefrome1-1
dc.relation.ispartofpagetoe1-9
dc.relation.ispartofissue10
dc.relation.ispartofjournalEAI Endorsed Transactions on Context-aware Systems and Applications
dc.relation.ispartofvolume3
dc.subject.fieldofresearchComputer Vision
dc.subject.fieldofresearchcode080104
dc.titleFourier Volume Registration based Dense 3D Mapping
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
dcterms.licensehttps://creativecommons.org/licenses/by/3.0/
dc.description.versionVersion of Record (VoR)
gro.facultyGriffith Sciences, School of Engineering and Built Environment
gro.rights.copyright© 2016 Luke Lincoln and Ruben Gonzalez, licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
gro.hasfulltextFull Text
gro.griffith.authorGonzalez, Ruben
gro.griffith.authorLincoln, Luke


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