An Improved Filtering Algorithm for Vehicle-borne Laser Scanning Point Cloud
In order to improve the filtering accuracy of vehicle-borne laser scanning point cloud,and at the same time,to improve the problems existing in the current point cloud filtering algorithm,such as many human-computer interactions and difficulty in controlling parameters,this paper proposes an improved Triangulated Irregular Network (TIN) iterative densifying filtering algorithm based on the classical TIN iterative densifying filtering algorithm,which optimizes the selection of ground seed points and adaptively changes the grid size. The main approaches of the improved filtering algorithm to realize non-ground point filtering are as follows:firstly,the grid is constructed for the original vehicle-borne point cloud data,and the ground seed points are determined by calculating the neighbor-hood convolution and constructing the virtual seed points;secondly,the laser point to be determined is densified into the TIN accord-ing to the setting threshold condition;finally,the ground points are extracted by adaptively changing the grid size and performing itera-tive filtering. The experiment is carried out on the point cloud data of two typical urban roads. The results show that the improved filte-ring algorithm has better filtering performance and better terrain adaptability.
万方数据
维普
