Automatic Extraction of Structural and Non-Structural Road Edges from Mobile Laser Scanning Data
Accurate road information is important for applications involving road maintenance, intelligent transportation, and road network updates. Mobile laser scanning (MLS) can effectively extract road information. However, accurately extracting road edges based on large-scale data for complex road conditi...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/19/22/5030 |
| _version_ | 1818035580352593920 |
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| author | Mengmeng Yang Xianlin Liu Kun Jiang Jingzhong Xu Peng Sheng Diange Yang |
| author_facet | Mengmeng Yang Xianlin Liu Kun Jiang Jingzhong Xu Peng Sheng Diange Yang |
| author_sort | Mengmeng Yang |
| collection | DOAJ |
| description | Accurate road information is important for applications involving road maintenance, intelligent transportation, and road network updates. Mobile laser scanning (MLS) can effectively extract road information. However, accurately extracting road edges based on large-scale data for complex road conditions, including both structural and non-structural road types, remains difficult. In this study, a robust method to automatically extract structural and non-structural road edges based on a topological network of laser points between adjacent scan lines and auxiliary surfaces is proposed. The extraction of road and curb points was achieved mainly from the roughness of the extracted surface, without considering traditional thresholds (e.g., height jump, slope, and density). Five large-scale road datasets, containing different types of road curbs and complex road scenes, were used to evaluate the practicality, stability, and validity of the proposed method via qualitative and quantitative analyses. Measured values of the correctness, completeness, and quality of extracted road edges were over 95.5%, 91.7%, and 90.9%, respectively. These results confirm that the proposed method can extract road edges from large-scale MLS datasets without the need for auxiliary information on intensity, image, or geographic data. The proposed method is effective regardless of whether the road width is fixed, the road is regular, and the existence of pedestrians and vehicles. Most importantly, the proposed method provides a valuable solution for road edge extraction that is useful for road authorities when developing intelligent transportation systems, such as those required by self-driving vehicles. |
| first_indexed | 2024-12-10T06:57:19Z |
| format | Article |
| id | doaj.art-22f46421568043bcaf51d9241796137d |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-12-10T06:57:19Z |
| publishDate | 2019-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-22f46421568043bcaf51d9241796137d2022-12-22T01:58:24ZengMDPI AGSensors1424-82202019-11-011922503010.3390/s19225030s19225030Automatic Extraction of Structural and Non-Structural Road Edges from Mobile Laser Scanning DataMengmeng Yang0Xianlin Liu1Kun Jiang2Jingzhong Xu3Peng Sheng4Diange Yang5State Key Laboratory of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, Beijing 100084, ChinaChinese Academy of Surveying and Mapping, Beijing 100830, ChinaState Key Laboratory of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, Beijing 100084, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, ChinaChinese Academy of Surveying and Mapping, Beijing 100830, ChinaState Key Laboratory of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, Beijing 100084, ChinaAccurate road information is important for applications involving road maintenance, intelligent transportation, and road network updates. Mobile laser scanning (MLS) can effectively extract road information. However, accurately extracting road edges based on large-scale data for complex road conditions, including both structural and non-structural road types, remains difficult. In this study, a robust method to automatically extract structural and non-structural road edges based on a topological network of laser points between adjacent scan lines and auxiliary surfaces is proposed. The extraction of road and curb points was achieved mainly from the roughness of the extracted surface, without considering traditional thresholds (e.g., height jump, slope, and density). Five large-scale road datasets, containing different types of road curbs and complex road scenes, were used to evaluate the practicality, stability, and validity of the proposed method via qualitative and quantitative analyses. Measured values of the correctness, completeness, and quality of extracted road edges were over 95.5%, 91.7%, and 90.9%, respectively. These results confirm that the proposed method can extract road edges from large-scale MLS datasets without the need for auxiliary information on intensity, image, or geographic data. The proposed method is effective regardless of whether the road width is fixed, the road is regular, and the existence of pedestrians and vehicles. Most importantly, the proposed method provides a valuable solution for road edge extraction that is useful for road authorities when developing intelligent transportation systems, such as those required by self-driving vehicles.https://www.mdpi.com/1424-8220/19/22/5030remote sensingmobile laser scanningroad edge detectiontopological network |
| spellingShingle | Mengmeng Yang Xianlin Liu Kun Jiang Jingzhong Xu Peng Sheng Diange Yang Automatic Extraction of Structural and Non-Structural Road Edges from Mobile Laser Scanning Data Sensors remote sensing mobile laser scanning road edge detection topological network |
| title | Automatic Extraction of Structural and Non-Structural Road Edges from Mobile Laser Scanning Data |
| title_full | Automatic Extraction of Structural and Non-Structural Road Edges from Mobile Laser Scanning Data |
| title_fullStr | Automatic Extraction of Structural and Non-Structural Road Edges from Mobile Laser Scanning Data |
| title_full_unstemmed | Automatic Extraction of Structural and Non-Structural Road Edges from Mobile Laser Scanning Data |
| title_short | Automatic Extraction of Structural and Non-Structural Road Edges from Mobile Laser Scanning Data |
| title_sort | automatic extraction of structural and non structural road edges from mobile laser scanning data |
| topic | remote sensing mobile laser scanning road edge detection topological network |
| url | https://www.mdpi.com/1424-8220/19/22/5030 |
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