Study on algorithm of road surface model optimization over point cloud data
In the light of difficulties to generate highly accurate TIN from vehicle borne LiDAR data,this paper proposed a method to optimize the irregular triangulation and build model in the road coordinate system through convolution filtering.This method uses the convolution operation in the road coordinate system to avoid the sparse distribution of urban road point clouds and the inconsistency of road surface characteristics that cannot be solved by traditional filtering methods.Combining with the occupying grid 3D convolution method,the reliability screening and elevation optimization of the triangulation seed points are carried out,and finally the construction of road surface Triangulated Irregular Network(TIN)is realized.The comparison experiment proved that the projection errors of the TINs constructed by this method on various roads are stable and better than other methods.This method can provide high-precision road surface DTM(Digital Terrain Model)for urban 3D real scene modeling,and road surface normal vector reference for the registration and update of LiDAR point clouds.
LiDAR point cloudroad surface model3D convolution filterheight estimationTIN
万方数据
维普
