AUTOMATIC ROAD MARKINGS EXTRACTION, CLASSIFICATION AND VECTORIZATION FROM MOBILE LASER SCANNING DATA
- 1State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China
- 2School of Geodesy and Geomatics, Wuhan University, Wuhan, China
- 3Sichuan Provincial Transport Department Highway Planning, Survey, Design And Research Institute, Chengdu, China
- 4Faculty of Information Engineering, China University of Geosciences, Wuhan, China
Keywords: Mobile Mapping Technology, Point clouds, Road markings extraction, Model matching, Vectorization
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.