GP-ICP: Ground Plane ICP for Mobile Robots

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dc.contributor.authorKim, Hyungjjinko
dc.contributor.authorSong, Seungwonko
dc.contributor.authorMyung, Hyunko
dc.date.accessioned2019-07-18T05:33:00Z-
dc.date.available2019-07-18T05:33:00Z-
dc.date.created2019-06-28-
dc.date.created2019-06-28-
dc.date.issued2019-06-
dc.identifier.citationIEEE ACCESS, v.7, no.1, pp.76599 - 76610-
dc.identifier.issn2169-3536-
dc.identifier.urihttp://hdl.handle.net/10203/263322-
dc.description.abstractIn this paper, we propose a robust point cloud registration method for ground vehicles. Given the vast developments in the field of autonomous vehicles, the use of point cloud data has increased. The simultaneous localization and mapping (SLAM) algorithm is typically used to generate sophisticated point cloud maps. In the SLAM algorithm, the quality of the map depends on the performance of loop closure algorithms. The iterative closest point (ICP) algorithm is widely used for loop closure of the point cloud. However, the ICP algorithm might not work well for ground vehicles because it was originally developed for 3D reconstruction in computer vision field. Therefore, this paper proposes a method to find robust matching correspondences in the ICP algorithm on ground vehicle conditions. The performance of the proposed method is compared with other conventional methods by using KITTI open datasets. The source code is publicly released on the Github website.-
dc.languageEnglish-
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC-
dc.titleGP-ICP: Ground Plane ICP for Mobile Robots-
dc.typeArticle-
dc.identifier.wosid000473365200001-
dc.identifier.scopusid2-s2.0-85068316454-
dc.type.rimsART-
dc.citation.volume7-
dc.citation.issue1-
dc.citation.beginningpage76599-
dc.citation.endingpage76610-
dc.citation.publicationnameIEEE ACCESS-
dc.identifier.doi10.1109/ACCESS.2019.2921676-
dc.contributor.localauthorMyung, Hyun-
dc.description.isOpenAccessY-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorIterative closest point-
dc.subject.keywordAuthorpoint cloud-
dc.subject.keywordAuthorautonomous vehicle-
dc.subject.keywordAuthorground plane condition-
dc.subject.keywordPlusSCAN REGISTRATION-
dc.subject.keywordPlusVISION-
dc.subject.keywordPlusLIDAR-

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