Research On LiDAR Point Cloud Filtering Algorithm Based On Spatial Feature And Density Clustering

Airborne LiDAR technology is widely used,and it has low requirements on external environment conditions,so it is an important means to quickly obtain large-scale high-precision information.At this stage,the research on the hardware system of airborne lidar technology is relatively mature,which can obtain a large amount of information and provide a resource library for airborne lidar data processing.Airborne lidar data processing still has some problems to be solved.Point cloud denoising and point cloud filtering are one of the key problems.For example,it is difficult to achieve high quality,high efficiency,and self-adaptation at the same time,or there are many parameters set and rely on existing experience.In this paper,the system composition and point cloud data characteristics of airborne lidar are studied,and various classical filtering algorithms are studied,which provides a theoretical basis for the algorithm proposed in this paper.The main research work of this paper is as follows:(1)The research status of airborne lidar at home and abroad is briefly introduced.The representative algorithms that are currently studied are analyzed,and the difficulties existing in the point cloud denoising algorithm and the point cloud filtering algorithm are summarized.(2)For isolated and disordered point clouds,a point cloud coarse filtering algorithm based on triangulation and density clustering is proposed.Delaunay triangulation and Mean-Shift algorithm are used to gradually extract low-density disordered points and smooth the terrain according to the common constraints of triangulation and density on point clouds.The algorithm is simple in principle,simple in operation,and few in parameters.It can simultaneously denoise point-like noise points and roughly filter point clouds,providing a rough digital elevation model for finer filtering.The relevant experiments show that the algorithm is feasible.(3)A filtering algorithm based on two-dimensional grid and density clustering is proposed by taking advantage of the unique density-connected properties of density clustering.Firstly,clustering is performed according to the elevation value of the point cloud,then the plane point cloud is screened,and finally the filtering is completed by DBSCAN spatial clustering.This process can reach the edge of the steep ridge,preserving the topographic relief.Compared with several classical filtering algorithms,the quantitative results show that the total error of the filtering algorithm proposed in this paper is lower than the average value,the accuracy is high,and it has the ability to adapt to different terrains to a certain extent.