This paper presents a voxel-based region growing method for automatic road surface extraction from mobile laser scanning point clouds in an expressway environment. The proposed method has three major steps: constructing a voxel model; extracting the road surface points by employing the voxel-based segmentation algorithm; refining the road boundary using the curb-based segmentation algorithm. To evaluate the accuracy of the proposed method, the two-point cloud datasets of two typical test sites in an expressway environment consisting of flat and bumpy surfaces with a high slope were used. The proposed algorithm extracted the road surface successfully with high accuracy. There was an average recall of 99.5%, the precision was 96.3%, and the F1 score was 97.9%. From the extracted road surface, a framework for the estimation of road roughness was proposed. Good agreement was achieved when comparing the results of the road roughness map with the visual image, indicating the feasibility and effectiveness of the proposed framework.

本文提出了一种基于体素的区域生长方法，用于从高速公路环境中的移动激光扫描点云中自动提取路面。所提出的方法包括三个主要步骤：建立体素模型；采用基于体素的分割算法提取路面点；使用基于路缘的分割算法完善道路边界。为了评估该方法的准确性，使用了高速公路环境中两个典型测试站点的两点云数据集，这些环境由具有高斜率的平坦和颠簸的表面组成。该算法成功地提取了路面。平均召回率为99.5％，准确性为96.3％，F1分数为97.9％。从提取的路面上，提出了估算道路不平度的框架。将道路不平整图的结果与视觉图像进行比较，可以很好地达成共识，表明所提出框架的可行性和有效性。