To tackle the problems of heavy calculation burden and low efficiency of the pothole extraction algorithm based on the scanning of three-dimensional (3D) laser point clouds, this study proposes a pothole extraction method based on RANSAC. Firstly, RANSAC is employed to calculate the cross-sectional baseline for the correction of cross-sectional data and preliminary identification of pothole points and their locations. Secondly, the local reference road surface near the pothole is calculated by RANSAC so that the pothole points and road surface points can be marked. Thirdly, the seed filling algorithm is used to solve the connected domain and calculate the set of pothole points. Finally, the edge of the pothole is extracted with the set of pothole points and an exhaustive analysis of the pothole data is made. The experimental results show that RANSAC can quickly scan cross-sectional point cloud data, with the processing time increased by 56.46% on average compared with that of the curvature feature point detection algorithm. It has a good effect on extracting the depth and area of potholes with high accuracy. The average error of depth and area is 4.73% and 4.50%, respectively.