Road Extraction from 3D Point Clouds based on the Difference of Normal Vector

Mobile Laser System and Airborne Laser System can quickly collect a large quantity of urban scene point cloud data in real time, the collected point cloud data is the main source of road area extraction. However, the obtained point cloud data are redundant and unordered discrete, which are challenging for efficient classification and extraction. In order to solve these problems, we propose a road extraction method based on the difference of normal vector: 1) preprocess the data for simplifying the point cloud, making the subsequent operations more efficiently; 2) use the progressive morphological filter to obtain the ground point cloud data, and then calculate the difference of normal vector for the clusters of the point cloud to get the preliminary road area; 3) leverage the random sample consensus plane fitting method to optimize the road area. The experimental results show that the proposed method can extract the road area accurately and quickly from the urban 3D point cloud data.