Extraction and classification of urban road marking lines based on point cloud features

ZHENG Shuaifeng; WANG Shangdong; ZHANG Chenyi; WANG Lunwei

doi:10.7510/jgjs.issn.1001-3806.2024.01.005

LASER TECHNOLOGY > 2024 > 48(1): 27-33. > DOI: 10.7510/jgjs.issn.1001-3806.2024.01.005

ZHENG Shuaifeng, WANG Shangdong, ZHANG Chenyi, WANG Lunwei. Extraction and classification of urban road marking lines based on point cloud features[J]. LASER TECHNOLOGY, 2024, 48(1): 27-33. DOI: 10.7510/jgjs.issn.1001-3806.2024.01.005

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Extraction and classification of urban road marking lines based on point cloud features

School of Earth Science and Engineering, Hohai University, Nanjing 211100, China

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Received Date: November 20, 2022
Revised Date: January 12, 2023
Published Date: January 24, 2024

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Abstract

In order to solve the problem of the low value of road marking line extraction integrity and accuracy based on vehicle-mounted light detection and ranging(LiDAR) point cloud data, a fast road marking line extraction method based on multiple features of the point cloud was proposed. Based on the strength information, geometric information, and semantic information of urban road marking lines, combined with the strength feature, elevation feature, and point density feature of the road surface point cloud, multiple geographic reference images were generated, and the feature extraction and filling of the multiple feature images were carried out, and then the Ostu algorithm and Alpha shapes algorithm were used to achieve the precise extraction of the road marking line point cloud. According to the geometric and semantic information of the marking line and the model matching scheme, the fine classification of the marking line was realized. The theoretical analysis and experimental verification were carried out, and the point cloud data of a city road in Australia was obtained. The results show that the accuracy of the extracted short dotted line, zebra line, one-way steering arrow and, the long dotted line is higher than 96%, the recall rate is 91% and above, and the comprehensive evaluation index is 94% and above. The study has contributed to the research in the field of driverless driving and also provided certain reference values for the construction of urban digital.

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