Data-driven MCMC sampling for vision-based 6D SLAM

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dc.contributor.authorMin, Ji-Hongko
dc.contributor.authorKim, Jung-Hoko
dc.contributor.authorShin, S.ko
dc.contributor.authorKweon, In-Soko
dc.date.accessioned2013-03-13T05:14:11Z-
dc.date.available2013-03-13T05:14:11Z-
dc.date.created2012-08-08-
dc.date.created2012-08-08-
dc.date.issued2012-06-
dc.identifier.citationELECTRONICS LETTERS, v.48, no.12, pp.687 - 689-
dc.identifier.issn0013-5194-
dc.identifier.urihttp://hdl.handle.net/10203/104540-
dc.description.abstractAn efficient sampling technique for estimating camera motion is presented. For this purpose, Markov chain Monte Carlo (MCMC) sampling is incorporated into the data-driven proposal distribution in order to improve the SLAM performance. Experimental results using both synthetic and real datasets demonstrate the efficiency of the proposed method.-
dc.languageEnglish-
dc.publisherINST ENGINEERING TECHNOLOGY-IET-
dc.titleData-driven MCMC sampling for vision-based 6D SLAM-
dc.typeArticle-
dc.identifier.wosid000305091900011-
dc.identifier.scopusid2-s2.0-84864186479-
dc.type.rimsART-
dc.citation.volume48-
dc.citation.issue12-
dc.citation.beginningpage687-
dc.citation.endingpage689-
dc.citation.publicationnameELECTRONICS LETTERS-
dc.identifier.doi10.1049/el.2012.0897-
dc.contributor.localauthorKweon, In-So-
dc.contributor.nonIdAuthorShin, S.-
dc.type.journalArticleArticle-
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