Curb recognition algorithm based on geometric characters and 3D point cloud features
Typical curb recognition algorithms have difficulty in balancing real-time performance and reliability. In this paper, with a multi-line LiDAR used, a curb recognition algorithm based on geometric features and 3D point cloud features of curb areas is proposed, which reaches a tradeoff between real-t...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
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Hebei University of Science and Technology
2019-12-01
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| Series: | Journal of Hebei University of Science and Technology |
| Subjects: | |
| Online Access: | http://xuebao.hebust.edu.cn/hbkjdx/ch/reader/create_pdf.aspx?file_no=b201906001&flag=1&journal_ |
| _version_ | 1818355135600918528 |
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| author | Junji CHEN Dawei PI Boyuan XIE Hongliang WANG Xia WANG |
| author_facet | Junji CHEN Dawei PI Boyuan XIE Hongliang WANG Xia WANG |
| author_sort | Junji CHEN |
| collection | DOAJ |
| description | Typical curb recognition algorithms have difficulty in balancing real-time performance and reliability. In this paper, with a multi-line LiDAR used, a curb recognition algorithm based on geometric features and 3D point cloud features of curb areas is proposed, which reaches a tradeoff between real-time performance and reliability. Faced with the large amount of point cloud data, the algorithm firstly proposes a ground segmentation method based on RANSAC algorithm, filtering out the ground points in the preset region of interest, and then the orderly rasterization of the remaining disordered points is carried out for matching and screening curb areas according to the curb's geometric characters and the points' distribution feature. After that, the least square method fused with RANSAC is proposed to achieve the robust fitting of curb curve. Experiments show that the recognition accuracy of the algorithm is more than 95% in both straight and bend scenes, and the time-consuming is less than 15 ms, which indicates the good accuracy and real-time performance of the proposed algorithm.The algorithm can effectively identify road curb, thus providing a theoretical reference and method basis for intelligent vehicle driving area recognition and its' control. |
| first_indexed | 2024-12-13T19:36:30Z |
| format | Article |
| id | doaj.art-abdea66fd2b949928b5549c177b462c6 |
| institution | Directory Open Access Journal |
| issn | 1008-1542 |
| language | zho |
| last_indexed | 2024-12-13T19:36:30Z |
| publishDate | 2019-12-01 |
| publisher | Hebei University of Science and Technology |
| record_format | Article |
| series | Journal of Hebei University of Science and Technology |
| spelling | doaj.art-abdea66fd2b949928b5549c177b462c62022-12-21T23:33:47ZzhoHebei University of Science and TechnologyJournal of Hebei University of Science and Technology1008-15422019-12-0140646146810.7535/hbkd.2019yx06002b201906001Curb recognition algorithm based on geometric characters and 3D point cloud featuresJunji CHEN0Dawei PI1Boyuan XIE2Hongliang WANG3Xia WANG4School of Mechanical Engineering, Nanjing University of Science and Technology,Nanjing, Jiangsu 210094, ChinaSchool of Mechanical Engineering, Nanjing University of Science and Technology,Nanjing, Jiangsu 210094, ChinaSchool of Mechanical Engineering, Nanjing University of Science and Technology,Nanjing, Jiangsu 210094, ChinaSchool of Mechanical Engineering, Nanjing University of Science and Technology,Nanjing, Jiangsu 210094, ChinaSchool of Mechanical Engineering, Nanjing University of Science and Technology,Nanjing, Jiangsu 210094, ChinaTypical curb recognition algorithms have difficulty in balancing real-time performance and reliability. In this paper, with a multi-line LiDAR used, a curb recognition algorithm based on geometric features and 3D point cloud features of curb areas is proposed, which reaches a tradeoff between real-time performance and reliability. Faced with the large amount of point cloud data, the algorithm firstly proposes a ground segmentation method based on RANSAC algorithm, filtering out the ground points in the preset region of interest, and then the orderly rasterization of the remaining disordered points is carried out for matching and screening curb areas according to the curb's geometric characters and the points' distribution feature. After that, the least square method fused with RANSAC is proposed to achieve the robust fitting of curb curve. Experiments show that the recognition accuracy of the algorithm is more than 95% in both straight and bend scenes, and the time-consuming is less than 15 ms, which indicates the good accuracy and real-time performance of the proposed algorithm.The algorithm can effectively identify road curb, thus providing a theoretical reference and method basis for intelligent vehicle driving area recognition and its' control.http://xuebao.hebust.edu.cn/hbkjdx/ch/reader/create_pdf.aspx?file_no=b201906001&flag=1&journal_sensor technologyintelligent vehiclecurbgeometric characters3d point cloudfusing ransac |
| spellingShingle | Junji CHEN Dawei PI Boyuan XIE Hongliang WANG Xia WANG Curb recognition algorithm based on geometric characters and 3D point cloud features Journal of Hebei University of Science and Technology sensor technology intelligent vehicle curb geometric characters 3d point cloud fusing ransac |
| title | Curb recognition algorithm based on geometric characters and 3D point cloud features |
| title_full | Curb recognition algorithm based on geometric characters and 3D point cloud features |
| title_fullStr | Curb recognition algorithm based on geometric characters and 3D point cloud features |
| title_full_unstemmed | Curb recognition algorithm based on geometric characters and 3D point cloud features |
| title_short | Curb recognition algorithm based on geometric characters and 3D point cloud features |
| title_sort | curb recognition algorithm based on geometric characters and 3d point cloud features |
| topic | sensor technology intelligent vehicle curb geometric characters 3d point cloud fusing ransac |
| url | http://xuebao.hebust.edu.cn/hbkjdx/ch/reader/create_pdf.aspx?file_no=b201906001&flag=1&journal_ |
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