In road network extraction using airborne LiDAR point clouds, the road intensity inconsistency issue results in more points similar to road intensity when using intensity to extract roads, and some real road points are also filtered out. In this paper, we propose a grid segmentation method for extracting road point clouds based on local intensity. The method first uses skewness balance to obtain the road-intensity threshold of the filtered ground points, divides the ground points into a grid, and calculates the grid’s average intensity value. The number of grids for intensity values of the central grid and its neighboring grids within the road-intensity threshold is used to calculate the road point cloud. Experiments conducted with actual data in a complex environment show that the method can effectively reduce the abnormal points around the road while obtaining a more complete road, providing reference for future road network extraction. Data1 in the extracted initial road has a completeness rate of 84.1%, and data2 has a completeness rate of 67.1%, which has obvious benefits compared with the traditional method.