Research on Urban Road Pavement Extraction Method Based on Vehicle Point Cloud Data

In order to extract structured urban road surface efficiently from vehicle point cloud data, this paper proposes a road surface extraction method based on normal vector clustering. First, a cloth simulation based filtering algorithm (CSF) is used to filter the interference of non-ground points; Secondly, the normal vector and curvature of each pavement point are estimated by principal component analysis; Finally, with the similarity of point cloud normal vector as the constraint condition, the road point cloud cluster is segmented. The vehicle-mounted point cloud data in the scene of straight and turning sections are verified, and the completeness and accuracy of road surface extraction are both above 92%. The experimental results show that the extraction results are less affected by the complex environment of urban roads, so the method proposed in this paper has strong applicability.