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

Abstract

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.