提取准确的道路信息对城市规划和数字城市制图具有重要意义。利用高密度车载激光扫描数据，针对无法以路沿作为道路边界的情况，提出一种基于边缘线检测的道路自动提取方法。先利用平面检测算法对分段后的路面完成粗提取，在此基础上，分析道路边缘线与相邻两侧路面在激光反射强度与几何性质上的差异，通过设定合理阈值提取边缘线上的点云，最后对离散边缘点进行曲线拟合完成道路精提取。利用实际获取的城区车载点云数据验证表明，该方法提取道路的正确度、完整度和提取质量等均高于90%，特别是对无明显路沿的道路，可有效识别出道路边缘线。
Extracting accurate road information is of vital importance in urban planning and digital city mapping. We used high-density point clouds collected by mobile laser scanning(MLS) systems, and proposed a novel approach based on road boundary detection in curb missing areas. First, the road was roughly extracted by using plane detection algorithm. Next, we analyzed the difference in laser reflection intensity and geometry attribution between road boundary and two sides of surface and retrieved the point clouds of boundary by setting reasonable threshold. Finally, the road surface was accurately extracted based on curve fitting of discrete points. To evaluate the performance of the proposed method, experiments were conducted using urban point clouds data. The extraction method achieves correctness, completeness, and quality rates of above 90%, which indicates that the proposed method effectively detects road boundary and extracts road surface accurately in curb missing urban areas.
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