It is difficult to extract road markings by using mobile LiDAR point cloud data.To solve this problem,this paper adopts an improved road marking classification and extraction method based on point cloud feature image.Firstly,the road point cloud is projected to generate the point cloud feature image.Through the image gradient analysis,image binarization and connected domain analysis,the road marking pixels are further extracted.Then,after back projection to 3D point cloud,Gaussian mixture model is used to finely optimize the road markings,so as to extract the complete road marking point cloud.Finally,the road marking point cloud is classified and extracted through template matc-hing classification.The experimental results show that the accuracy,integrity and comprehensive e-valuation of road markings extracted by this method in different road environments exceed 90％.
维普
万方数据
