I-LOAM: Intensity Enhanced LiDAR Odometry and Mapping

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dc.contributor.authorPark, Yeong Sangko
dc.contributor.authorJang, Hyesuko
dc.contributor.authorKim, Ayoungko
dc.date.accessioned2021-01-28T06:10:05Z-
dc.date.available2021-01-28T06:10:05Z-
dc.date.created2020-11-30-
dc.date.issued2020-06-23-
dc.identifier.citation17th International Conference on Ubiquitous Robots, UR 2020, pp.455 - 458-
dc.identifier.urihttp://hdl.handle.net/10203/280176-
dc.description.abstractIn this paper, we introduce an extension to the existing LiDAR Odometry and Mapping (LOAM) [1] by additionally considering LiDAR intensity. In an urban environment, planar structures from buildings and roads often introduce ambiguity in a certain direction. Incorporation of the intensity value to the cost function prevents divergence occurence from this structural ambiguity, thereby yielding better odometry and mapping in terms of accuracy. Specifically, we have updated the edge and plane point correspondence search to include intensity. This simple but effective strategy shows meaningful improvement over the existing LOAM. The proposed method is validated using the KITTI dataset.-
dc.languageEnglish-
dc.publisherInstitute of Electrical and Electronics Engineers Inc.-
dc.titleI-LOAM: Intensity Enhanced LiDAR Odometry and Mapping-
dc.typeConference-
dc.identifier.scopusid2-s2.0-85094324796-
dc.type.rimsCONF-
dc.citation.beginningpage455-
dc.citation.endingpage458-
dc.citation.publicationname17th International Conference on Ubiquitous Robots, UR 2020-
dc.identifier.conferencecountryJA-
dc.identifier.conferencelocationVirtual-
dc.identifier.doi10.1109/UR49135.2020.9144987-
dc.contributor.localauthorKim, Ayoung-
dc.contributor.nonIdAuthorPark, Yeong Sang-
dc.contributor.nonIdAuthorJang, Hyesu-
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CE-Conference Papers(학술회의논문)
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