DC Field | Value | Language |
---|---|---|
dc.contributor.author | Sung, Changki | ko |
dc.contributor.author | Jeon, Seulgi | ko |
dc.contributor.author | Lim, HyungTae | ko |
dc.contributor.author | Myung, Hyun | ko |
dc.date.accessioned | 2022-06-02T01:00:52Z | - |
dc.date.available | 2022-06-02T01:00:52Z | - |
dc.date.created | 2021-11-24 | - |
dc.date.issued | 2022-04 | - |
dc.identifier.citation | INTELLIGENT SERVICE ROBOTICS, v.15, no.2, pp.161 - 170 | - |
dc.identifier.issn | 1861-2776 | - |
dc.identifier.uri | http://hdl.handle.net/10203/296750 | - |
dc.description.abstract | Accurate localization and mapping in a large-scale environment is an essential system of an autonomous vehicle. The difficulty of the previous LiDAR or LiDAR-inertial simultaneous localization and mapping (SLAM) methods is correcting long-term drift error in a large-scale environment. This paper proposes a novel approach of a large-scale, graph-based SLAM with traffic sign data involved in a high-definition (HD) map. The graph of the system is structured with the inertial measurement unit (IMU) factor, LiDAR-inertial odometry factor, map-matching factor, and loop closure factor. The local sliding window-based optimization method is employed for real-time processing. As a result, the proposed method improves the accuracy of the localization and mapping compared with the state-of-the-art LiDAR or LiDAR-inertial SLAM methods. In addition, the proposed method can localize accurately without revisit, required for conventional graph-based SLAM for graph optimization, unlike previous studies. The proposed method is intensively validated with a data set collected in a city where the Global Navigation Satellite System (GNSS) signal is unreliable and on a university campus. | - |
dc.language | English | - |
dc.publisher | SPRINGER HEIDELBERG | - |
dc.title | What If There Was No Revisit? Large-Scale Graph-based SLAM with Traffic Sign Detection in an HD Map Using LiDAR Inertial Odometry | - |
dc.type | Article | - |
dc.identifier.wosid | 000722531000001 | - |
dc.identifier.scopusid | 2-s2.0-85119883895 | - |
dc.type.rims | ART | - |
dc.citation.volume | 15 | - |
dc.citation.issue | 2 | - |
dc.citation.beginningpage | 161 | - |
dc.citation.endingpage | 170 | - |
dc.citation.publicationname | INTELLIGENT SERVICE ROBOTICS | - |
dc.identifier.doi | 10.1007/s11370-021-00395-2 | - |
dc.contributor.localauthor | Myung, Hyun | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Autonomous vehicle | - |
dc.subject.keywordAuthor | 3D LiDAR SLAM | - |
dc.subject.keywordAuthor | HD map | - |
dc.subject.keywordAuthor | 3D point cloud map | - |
dc.subject.keywordPlus | REGISTRATION | - |
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