Abstract
Abstract. To meet the demands of various applications such as high definition navigation map production for unmanned vehicles and road reconstruction and expansion engineering, this paper proposes an effective and efficient approach to automatically extract, classify and vectorize road markings from Mobile Laser Scanning (MLS) point clouds. Firstly, the MLS point cloud is segmented to ground and non-ground points. Secondly, several geo-reference images are generated and further used to detect road markings pixels under an image processing scheme. Thirdly, road marking point clouds are retrieved from the image and further segmented into connected objects. Otsu thresholding and Statistic Outlier Remover are adopted to refine the road marking objects. Next, each road marking objects are classified into several categories such as boundary lines, rectangle road markings, etc. based on its bounding box information. Other irregular road markings are classified by a model matching scheme. Finally, all classified road markings are vectorized as closed or unclosed polylines after reconnecting the breaking boundary lines. Comprehensive experiments are done on various MLS point clouds of both the urban and highway scenarios, which show that the precision and recall of the proposed method is higher than 95% for road marking extraction and as high as 93% for road marking classification on highway scenarios. The ratio is 92% and 85% for urban scenarios.
Highlights
Recent years, the development of Light Detection And Ranging (LiDAR) and Mobile Mapping Technology (MMT) made it feasible for us to achieve accurate 3D perception of the world with high efficiency
The research in literature related to road marking operation from mobile laser scanning (MLS) data can be roughly divided into detection, classification and modeling
The proposed method efficiently vectorize the road markings based on their bounding boxes information and the alignment result of model matching, which make up the gap of vectorization on road marking detection topic
Summary
The development of Light Detection And Ranging (LiDAR) and Mobile Mapping Technology (MMT) made it feasible for us to achieve accurate 3D perception of the world with high efficiency. Various novel applications such as high definition navigation maps and highway reconstruction and expansion engineering are expecting for more detailed 3D data with semantic information to get involved. High-definition maps with higher accuracy and richer detailed road information can assist unmanned vehicles to achieve high-precision positioning, perceive the driving environment ahead, and accomplish accurate decision-making and control. Methods that can automatically detect, classify and model the road markings from point cloud data with high accuracy, robustness and efficiency are urgent to be proposed
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