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As research on autonomous driving deepens, High-definition Maps (HD Maps) have gradually become an auxiliary information for the new generation of autonomous driving technology. Compared to traditional electronic navigation maps, HD Maps have higher accuracy requirements and more information. Multi-road environment information and road elements are included. In the production of HD Maps, the on-board Mobile Laser Scanning (MLS) system has the ability to quickly collect environmental information, with high precision, thus making the system a widely used data collection method today. However, subsequent map building, digitization, and other mapping work still rely on manual operation, which is time-consuming and laborious. Therefore, this research is dedicated to developing a semi-automatic algorithm to generate HD Maps from the acquired point cloud data. This research focuses on the extraction of road surface markings, using the Cloth Simulation Filter (CSF) to obtain the road surface point cloud to improve the extraction efficiency. The road markings are extracted using the characteristic of high intensity values, and the commonly used Otsu threshold filter in image processing is used to extract point clouds with high reflectance intensity, eliminating the need for manual setting of point clouds. And based on geometric conditions, the objects are classified, such as arrow lines, pedestrian crossings, stop lines, and lane lines, which are convenient for further mapping HD Maps. |
