Evaluation of 3D point cloud for autonomous vehicle（3D point cloud evaluation index for detecting the double structure）
Localization in autonomous vehicles is an important technology, and the use of 3D point clouds, which provide accurate information on the road surroundings, has been attracting attention to help improve localization. In recent years, many methods for constructing 3D point clouds have been proposed f...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | Japanese |
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The Japan Society of Mechanical Engineers
2020-12-01
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| Series: | Nihon Kikai Gakkai ronbunshu |
| Subjects: | |
| Online Access: | https://www.jstage.jst.go.jp/article/transjsme/86/892/86_20-00151/_pdf/-char/en |
| _version_ | 1798015036726181888 |
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| author | Takaya MURAKAMI Yuki KITSUKAWA Eijiro TAKEUCHI Yoshiki NINOMIYA Junichi MEGURO |
| author_facet | Takaya MURAKAMI Yuki KITSUKAWA Eijiro TAKEUCHI Yoshiki NINOMIYA Junichi MEGURO |
| author_sort | Takaya MURAKAMI |
| collection | DOAJ |
| description | Localization in autonomous vehicles is an important technology, and the use of 3D point clouds, which provide accurate information on the road surroundings, has been attracting attention to help improve localization. In recent years, many methods for constructing 3D point clouds have been proposed for use in autonomous vehicles. However, 3D point clouds can be misaligned due to errors in measurement high accurate sensors, and so on, which causes the failure of localization. Therefore, it is important to confirm the accuracy of the 3D point clouds and the feasibility of high-accuracy localization in advance. The accuracy of 3D point clouds is often confirmed via simulations using sensor data collected by vehicles if the localization is sufficiently accurate. However, the applications of 3D point clouds are expanding, and it would be preferable to avoid using sensor data for misalignment detection. Therefore, in this paper, we propose an indicator to detect the location of the misalignment of a 3D point cloud constructed by Mobile Mapping System, using only the 3D point cloud as an indicator of convergence and similarity of the ground objects via matching. The effectiveness of the proposed method was confirmed by the evaluation test, which showed that the proposed method can detect positional shifts in 3D point clouds even at locations containing similarities among geological features. |
| first_indexed | 2024-04-11T15:27:55Z |
| format | Article |
| id | doaj.art-69ccfb8f1bd04a90a1df6288d0f9bbfb |
| institution | Directory Open Access Journal |
| issn | 2187-9761 |
| language | Japanese |
| last_indexed | 2024-04-11T15:27:55Z |
| publishDate | 2020-12-01 |
| publisher | The Japan Society of Mechanical Engineers |
| record_format | Article |
| series | Nihon Kikai Gakkai ronbunshu |
| spelling | doaj.art-69ccfb8f1bd04a90a1df6288d0f9bbfb2022-12-22T04:16:12ZjpnThe Japan Society of Mechanical EngineersNihon Kikai Gakkai ronbunshu2187-97612020-12-018689220-0015120-0015110.1299/transjsme.20-00151transjsmeEvaluation of 3D point cloud for autonomous vehicle（3D point cloud evaluation index for detecting the double structure）Takaya MURAKAMI0Yuki KITSUKAWA1Eijiro TAKEUCHI2Yoshiki NINOMIYA3Junichi MEGURO4Division of Mechatronics Engineering, Graduate School of Science and Technology, Meijo UniversityGraduate School of Information Science, Nagoya UniversityGraduate School of Informatics, Nagoya UniversityInstitute of Innovation for Future Society Nagoya UniversityGraduate School of Mechatronics, Faculty of Science and Technology, Meijo UniversityLocalization in autonomous vehicles is an important technology, and the use of 3D point clouds, which provide accurate information on the road surroundings, has been attracting attention to help improve localization. In recent years, many methods for constructing 3D point clouds have been proposed for use in autonomous vehicles. However, 3D point clouds can be misaligned due to errors in measurement high accurate sensors, and so on, which causes the failure of localization. Therefore, it is important to confirm the accuracy of the 3D point clouds and the feasibility of high-accuracy localization in advance. The accuracy of 3D point clouds is often confirmed via simulations using sensor data collected by vehicles if the localization is sufficiently accurate. However, the applications of 3D point clouds are expanding, and it would be preferable to avoid using sensor data for misalignment detection. Therefore, in this paper, we propose an indicator to detect the location of the misalignment of a 3D point cloud constructed by Mobile Mapping System, using only the 3D point cloud as an indicator of convergence and similarity of the ground objects via matching. The effectiveness of the proposed method was confirmed by the evaluation test, which showed that the proposed method can detect positional shifts in 3D point clouds even at locations containing similarities among geological features.https://www.jstage.jst.go.jp/article/transjsme/86/892/86_20-00151/_pdf/-char/enlocalizationmap evaluation3d point cloudsnormal distributions transformautonomous vehicle |
| spellingShingle | Takaya MURAKAMI Yuki KITSUKAWA Eijiro TAKEUCHI Yoshiki NINOMIYA Junichi MEGURO Evaluation of 3D point cloud for autonomous vehicle（3D point cloud evaluation index for detecting the double structure） Nihon Kikai Gakkai ronbunshu localization map evaluation 3d point clouds normal distributions transform autonomous vehicle |
| title | Evaluation of 3D point cloud for autonomous vehicle（3D point cloud evaluation index for detecting the double structure） |
| title_full | Evaluation of 3D point cloud for autonomous vehicle（3D point cloud evaluation index for detecting the double structure） |
| title_fullStr | Evaluation of 3D point cloud for autonomous vehicle（3D point cloud evaluation index for detecting the double structure） |
| title_full_unstemmed | Evaluation of 3D point cloud for autonomous vehicle（3D point cloud evaluation index for detecting the double structure） |
| title_short | Evaluation of 3D point cloud for autonomous vehicle（3D point cloud evaluation index for detecting the double structure） |
| title_sort | evaluation of 3d point cloud for autonomous vehicle 3d point cloud evaluation index for detecting the double structure |
| topic | localization map evaluation 3d point clouds normal distributions transform autonomous vehicle |
| url | https://www.jstage.jst.go.jp/article/transjsme/86/892/86_20-00151/_pdf/-char/en |
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