Image feature-based real-time RGB-D 3D SLAM with GPU accelerationGPU 가속화를 통한 이미지 특징점 기반 RGB-D 3차원 SLAM

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dc.contributor.authorLee, Donghwako
dc.contributor.authorKim, Hyongjinko
dc.contributor.authorMyung, Hyunko
dc.date.accessioned2013-08-08T05:10:54Z-
dc.date.available2013-08-08T05:10:54Z-
dc.date.created2013-07-25-
dc.date.created2013-07-25-
dc.date.created2013-07-25-
dc.date.issued2013-05-
dc.identifier.citationJournal of Institute of Control, Robotics and Systems, v.19, no.5, pp.457 - 461-
dc.identifier.issn1976-5622-
dc.identifier.urihttp://hdl.handle.net/10203/174414-
dc.description.abstractThis paper proposes an image feature-based real-time RGB-D (Red-Green-Blue Depth) 3D SLAM (Simultaneous Localization and Mapping) system. RGB-D data from Kinect style sensors contain a 2D image and per-pixel depth information. 6-DOF (Degree-of-Freedom) visual odometry is obtained through the 3D-RANSAC (RANdom SAmple Consensus) algorithm with 2D image features and depth data. For speed up extraction of features, parallel computation is performed with GPU acceleration. After a feature manager detects a loop closure, a graph-based SLAM algorithm optimizes trajectory of the sensor and builds a 3D point cloud based map.-
dc.languageKorean-
dc.publisherInstitute of Control, Robotics and Systems-
dc.titleImage feature-based real-time RGB-D 3D SLAM with GPU acceleration-
dc.title.alternativeGPU 가속화를 통한 이미지 특징점 기반 RGB-D 3차원 SLAM-
dc.typeArticle-
dc.identifier.scopusid2-s2.0-84887165572-
dc.type.rimsART-
dc.citation.volume19-
dc.citation.issue5-
dc.citation.beginningpage457-
dc.citation.endingpage461-
dc.citation.publicationnameJournal of Institute of Control, Robotics and Systems-
dc.embargo.liftdate9999-12-31-
dc.embargo.terms9999-12-31-
dc.contributor.localauthorMyung, Hyun-
dc.contributor.nonIdAuthorKim, Hyongjin-
dc.subject.keywordAuthor3D SLAM-
dc.subject.keywordAuthorImage feature-
dc.subject.keywordAuthorRGB-D-
dc.subject.keywordAuthorSLAM-
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