Vehicle-based laser scanning is extensively used for urban three-dimensional data acquisition because of its advantages of fast, high accuracy, and high density. However, it is not easy to accurately and efficiently extract urban road point clouds because of the large amount of data and multiple targets in urban scenarios. Based on the progressive morphological filtering algorithm, this study proposes an algorithm using grid approximation rather than three-dimensional space point operation and adaptive calculation of filtering parameters. As per the spatial distribution characteristics of urban roads, using the driving track information of scanning vehicles, the road boundary points are extracted using normal vector clustering, distance constraint, and continuity distribution constraint methods. Moreover, the accurate road boundary is generated by result clustering and fitting to achieve fast and accurate extraction of the road point cloud. The experimental results demonstrate that the accuracy, integrity, and overall quality of the road boundary extracted using the proposed algorithm are >90%. This shows that the difference between the boundary position and detected value is <3 cm.