Aiming at the low accuracy of road data information obtained by airborne LiDAR, an algorithm for extracting and segmenting road information by dynamic fitting based on low-altitude scanning three-dimensional point cloud data of UAV (Unmanned Aerial Vehicle) is proposed. Firstly, the principal component analysis algorithm is used to obtain the normal vector of road point data. Then, combining elevation information with normal vector information, the range of road elevation and normal vector is obtained by clustering algorithm, and the road point cloud data is extracted afterwards by range. Secondly, polynomial fitting is used to model the road data. Then the dynamic polynomial fitting is used to extract the data of the whole section of road surface, assets on the road, pedestrian and vehicle data. Finally, the region growth algorithm is used to segment the assets and pedestrian vehicle data on the road surface. The experiment shows that the proposed algorithm has a strong anti-interference ability to block objects on the road. It can extract the road surface and segment the data on the road surface. The proposed algorithm in this study is more sensitive to the road surface data by comparing with the region growth algorithm.