Automatic Vector-Based Road Structure Mapping Using Multibeam LiDAR
The high-definition map (HD-map) of road structures is crucial for the safe planning and control of autonomous vehicles. However, generating and updating such maps requires intensive manual work. Simultaneous localization and mapping (SLAM) is able to automatically build and update a map of the envi...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-07-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/11/14/1726 |
| _version_ | 1818959078531006464 |
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| author | Junqiao Zhao Xudong He Jun Li Tiantian Feng Chen Ye Lu Xiong |
| author_facet | Junqiao Zhao Xudong He Jun Li Tiantian Feng Chen Ye Lu Xiong |
| author_sort | Junqiao Zhao |
| collection | DOAJ |
| description | The high-definition map (HD-map) of road structures is crucial for the safe planning and control of autonomous vehicles. However, generating and updating such maps requires intensive manual work. Simultaneous localization and mapping (SLAM) is able to automatically build and update a map of the environment. Nevertheless, there is still a lack of SLAM method for generating vector-based road structure maps. In this paper, we propose a vector-based SLAM method for the road structure mapping using vehicle-mounted multibeam LiDAR. We propose using polylines as the primary mapping element instead of grid maps or point clouds because the vector-based representation is lightweight and precise. We explored the following: (1) the extraction and vectorization of road structures based on multiframe probabilistic fusion; (2) the efficient vector-based matching between frames of road structures; (3) the loop closure and optimization based on the pose-graph; and (4) the global reconstruction of the vector map. One specific road structure, the road boundary, is taken as an example. We applied the proposed mapping method to three road scenes, ranging from hundreds of meters to over ten kilometers and the results are automatically generated vector-based road boundary maps. The average absolute pose error of the trajectory in the mapping is 1.83 m without the aid of high-precision GPS. |
| first_indexed | 2024-12-20T11:35:55Z |
| format | Article |
| id | doaj.art-8fd5e62c5f05485192bb58adef40b8ff |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-20T11:35:55Z |
| publishDate | 2019-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-8fd5e62c5f05485192bb58adef40b8ff2022-12-21T19:42:07ZengMDPI AGRemote Sensing2072-42922019-07-011114172610.3390/rs11141726rs11141726Automatic Vector-Based Road Structure Mapping Using Multibeam LiDARJunqiao Zhao0Xudong He1Jun Li2Tiantian Feng3Chen Ye4Lu Xiong5MOE Key Laboratory of Embedded System and Service Computing, and the Department of Computer Science and Technology, School of Electronics and Information Engineering, Tongji University, Shanghai 201804, ChinaMOE Key Laboratory of Embedded System and Service Computing, and the Department of Computer Science and Technology, School of Electronics and Information Engineering, Tongji University, Shanghai 201804, ChinaMOE Key Laboratory of Embedded System and Service Computing, and the Department of Computer Science and Technology, School of Electronics and Information Engineering, Tongji University, Shanghai 201804, ChinaSchool of Surveying and Geo-Informatics, Tongji University, Shanghai 201804, ChinaMOE Key Laboratory of Embedded System and Service Computing, and the Department of Computer Science and Technology, School of Electronics and Information Engineering, Tongji University, Shanghai 201804, ChinaThe Institute of Intelligent Vehicles, Tongji University, Shanghai 201804, ChinaThe high-definition map (HD-map) of road structures is crucial for the safe planning and control of autonomous vehicles. However, generating and updating such maps requires intensive manual work. Simultaneous localization and mapping (SLAM) is able to automatically build and update a map of the environment. Nevertheless, there is still a lack of SLAM method for generating vector-based road structure maps. In this paper, we propose a vector-based SLAM method for the road structure mapping using vehicle-mounted multibeam LiDAR. We propose using polylines as the primary mapping element instead of grid maps or point clouds because the vector-based representation is lightweight and precise. We explored the following: (1) the extraction and vectorization of road structures based on multiframe probabilistic fusion; (2) the efficient vector-based matching between frames of road structures; (3) the loop closure and optimization based on the pose-graph; and (4) the global reconstruction of the vector map. One specific road structure, the road boundary, is taken as an example. We applied the proposed mapping method to three road scenes, ranging from hundreds of meters to over ten kilometers and the results are automatically generated vector-based road boundary maps. The average absolute pose error of the trajectory in the mapping is 1.83 m without the aid of high-precision GPS.https://www.mdpi.com/2072-4292/11/14/1726HD mapSLAMvectorizationautonomous drivingroad structure |
| spellingShingle | Junqiao Zhao Xudong He Jun Li Tiantian Feng Chen Ye Lu Xiong Automatic Vector-Based Road Structure Mapping Using Multibeam LiDAR Remote Sensing HD map SLAM vectorization autonomous driving road structure |
| title | Automatic Vector-Based Road Structure Mapping Using Multibeam LiDAR |
| title_full | Automatic Vector-Based Road Structure Mapping Using Multibeam LiDAR |
| title_fullStr | Automatic Vector-Based Road Structure Mapping Using Multibeam LiDAR |
| title_full_unstemmed | Automatic Vector-Based Road Structure Mapping Using Multibeam LiDAR |
| title_short | Automatic Vector-Based Road Structure Mapping Using Multibeam LiDAR |
| title_sort | automatic vector based road structure mapping using multibeam lidar |
| topic | HD map SLAM vectorization autonomous driving road structure |
| url | https://www.mdpi.com/2072-4292/11/14/1726 |
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