针对道路养护管理中缺乏车辙精确边界自动分析提取的问题，提出一种基于点云分水岭算法的路面车辙三维轮廓提取方法。首先对单车道路面激光点云数据进行高程归一化处理，消除路面横向设计坡度影响；利用包络线算法计算路面横断面的相对深度，生成路面点云相对深度模型，用于增强车辙辙槽的凹陷特征，并消除路面纵向坡度对点云分水岭算法的影响；然后，通过计算车辙边界曲率特征获取车辙粗略分界线，据此将路面划分为5个区域，选择每条横断面在对应区域相对深度最大的点作为点云分水岭算法的注水点集；最后，根据分水岭算法积水浸没的原理精确获取车辙的纵向轮廓。利用青岛平度市内S218省道的路面车辙激光点云数据进行实验分析。结果表明：所提方法可准确提取多类型的车辙边界，获取的车辙轮廓纵向边界均方根误差小于5 cm；同时，基于车辙边界获得的车辙深度均方根误差均小于1.5 mm，包络线法计算的车辙深度均方根误差均大于1.5 mm，所提方法具有更高的精度。所提方法为道路养护管理提供了一种有效的车辙三维轮廓提取方法。

This study aims at the lack of automatic analysis and extraction of precise road rut boundaries in road maintenance and management and proposes a method of a three-dimensional (3D) rut-contour extraction based on a point cloud watershed algorithm to address the issue. The method first operated the elevation normalization processing for the laser point cloud data of single-lane pavement to eliminate the influence of the transverse slope. The envelope method was employed to estimate the relative depth of the pavement cross-section and generate the relative depth model of the pavement point cloud. This was further utilized to enhance the sag characteristics of rutting grooves and remove the influence of the longitudinal slope of pavement on the watershed algorithm of the point cloud. Then, the rough boundary line of the rut was obtained by calculating the curvature characteristics of the rut boundary. Besides, the road surface was divided into five regions. The point with the maximum relative depth of each cross-section in the corresponding region was selected as the water-injection point set of the point cloud watershed algorithm. Finally, the longitudinal contour of ruts was precisely obtained according to the principle of waterlogging in the watershed algorithm. An experimental analysis was conducted on the S218 provincial road in Pingdu city, Qingdao to verify the corresponding laser point cloud data of the road ruts. The results show that the proposed method can accurately extract the multitype rut boundary, and the root mean square error of the longitudinal boundary of the rutting contour obtained is less than 5 cm. Simultaneously, the root mean square error of the rut depth obtained based on the rut boundary is less than 1.5 mm, while that estimated using the envelope method is greater than 1.5 mm. This demonstrates the higher accuracy of the proposed method. Therefore, the proposed method provides an effective method to extract the 3D contour of ruts for road maintenance and management.