Detection of Obstacles in Tunnel Based on Vehicle-borne LiDAR
Massive point clouds introduced by tunnel wall reflection can easily cause false alarms in LiDAR-based detection of obstacles in tunnel environment. A vehicle-borne LiDAR based obstacle-in-tunnel detection methodology is proposed in this paper. Firstly, a strategy of removing background point cloud...
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Editorial Office of Control and Information Technology
2021-01-01
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| Series: | Kongzhi Yu Xinxi Jishu |
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| Online Access: | http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2021.01.100 |
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| author | ZENG Xiang JIANG Guotao BAO Jiyu LIU Bangfan XIAO Zhihong |
| author_facet | ZENG Xiang JIANG Guotao BAO Jiyu LIU Bangfan XIAO Zhihong |
| author_sort | ZENG Xiang |
| collection | DOAJ |
| description | Massive point clouds introduced by tunnel wall reflection can easily cause false alarms in LiDAR-based detection of obstacles in tunnel environment. A vehicle-borne LiDAR based obstacle-in-tunnel detection methodology is proposed in this paper. Firstly, a strategy of removing background point cloud is designed. 2D grid map is generated from 3D point cloud, and grids corresponding to the tunnel boundary or the ground are labeled respectively. Based on the Euclidean clustering algorithm, the point cloud corresponding to the tunnel boundary is extracted. With the estimation of the parameters of the boundary curves, point cloud corresponding to the tunnel boundary is further removed. Similarily, the point cloud corresponding to the ground is also removed based on the estimation of parameters of a space plane. Subsequently, the obstacles are extracted independently from the remaining point cloud by Euclidean clustering, followed by the estimation of the position and dimensions of all those obstacles. Finally, the obstacles tracking is achieved by means of the global nearest neighbor algorithm with improved distance metric and the Kalman filter, and the track of all obstacles is updated by a customized life state transition strategy. Experimental results show that the proposed method can eliminate the interference of background point cloud effectively and yield stable results of obstacles identification and tracking. |
| format | Article |
| id | doaj-art-ffc8ac8a616b4f2bb4a5474b8f050b99 |
| institution | Kabale University |
| issn | 2096-5427 |
| language | zho |
| publishDate | 2021-01-01 |
| publisher | Editorial Office of Control and Information Technology |
| record_format | Article |
| series | Kongzhi Yu Xinxi Jishu |
| spelling | doaj-art-ffc8ac8a616b4f2bb4a5474b8f050b992025-08-25T06:52:53ZzhoEditorial Office of Control and Information TechnologyKongzhi Yu Xinxi Jishu2096-54272021-01-01381882315956Detection of Obstacles in Tunnel Based on Vehicle-borne LiDARZENG XiangJIANG GuotaoBAO JiyuLIU BangfanXIAO ZhihongMassive point clouds introduced by tunnel wall reflection can easily cause false alarms in LiDAR-based detection of obstacles in tunnel environment. A vehicle-borne LiDAR based obstacle-in-tunnel detection methodology is proposed in this paper. Firstly, a strategy of removing background point cloud is designed. 2D grid map is generated from 3D point cloud, and grids corresponding to the tunnel boundary or the ground are labeled respectively. Based on the Euclidean clustering algorithm, the point cloud corresponding to the tunnel boundary is extracted. With the estimation of the parameters of the boundary curves, point cloud corresponding to the tunnel boundary is further removed. Similarily, the point cloud corresponding to the ground is also removed based on the estimation of parameters of a space plane. Subsequently, the obstacles are extracted independently from the remaining point cloud by Euclidean clustering, followed by the estimation of the position and dimensions of all those obstacles. Finally, the obstacles tracking is achieved by means of the global nearest neighbor algorithm with improved distance metric and the Kalman filter, and the track of all obstacles is updated by a customized life state transition strategy. Experimental results show that the proposed method can eliminate the interference of background point cloud effectively and yield stable results of obstacles identification and tracking.http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2021.01.100environment perceptionLiDARobstacles identificationpoint cloudtunnelparameter estimationdistance metriclife state transition |
| spellingShingle | ZENG Xiang JIANG Guotao BAO Jiyu LIU Bangfan XIAO Zhihong Detection of Obstacles in Tunnel Based on Vehicle-borne LiDAR Kongzhi Yu Xinxi Jishu environment perception LiDAR obstacles identification point cloud tunnel parameter estimation distance metric life state transition |
| title | Detection of Obstacles in Tunnel Based on Vehicle-borne LiDAR |
| title_full | Detection of Obstacles in Tunnel Based on Vehicle-borne LiDAR |
| title_fullStr | Detection of Obstacles in Tunnel Based on Vehicle-borne LiDAR |
| title_full_unstemmed | Detection of Obstacles in Tunnel Based on Vehicle-borne LiDAR |
| title_short | Detection of Obstacles in Tunnel Based on Vehicle-borne LiDAR |
| title_sort | detection of obstacles in tunnel based on vehicle borne lidar |
| topic | environment perception LiDAR obstacles identification point cloud tunnel parameter estimation distance metric life state transition |
| url | http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2021.01.100 |
| work_keys_str_mv | AT zengxiang detectionofobstaclesintunnelbasedonvehiclebornelidar AT jiangguotao detectionofobstaclesintunnelbasedonvehiclebornelidar AT baojiyu detectionofobstaclesintunnelbasedonvehiclebornelidar AT liubangfan detectionofobstaclesintunnelbasedonvehiclebornelidar AT xiaozhihong detectionofobstaclesintunnelbasedonvehiclebornelidar |