Research On Reconstruction Method Of 3D Dense Point Cloud Based On Roundly Swinging Lidar

In the large scale scenes,three dimensional map of the environment,as the basic data,is playing an important role in automated manufacturing,auto-driving,intelligent transportation and other related fields.With the development of 3D laser scanning technology,utilization of laser scanning technology for 3D reconstruction draw extensive attention in many fields,and how to rebuild the environment precisely,uniformly and densely is one of important subjects.Therefore,a reconstruction method of 3D dense point cloud based on roundly swinging Lidar is proposed,which mainly includes view adjustable roundly swinging Lidar 3D scanning system,3D density analysis method of point cloud,and a corresponding point cloud matching method.To rebuild the large space scale scene precisely,uniformly and densely,a view adjustable roundly swinging Lidar 3D scanning system is proposed and designed.According to the analysis of application scene and the required quality of point cloud data,the schematic design is proceeded,and the system design and assembling are completed by then.The field experiment proves scanning system capable of reconstructing large scale scene steadily in high performance.In addition,aiming at changing the situation of lacking an effective method for analyzing 3D density distribution of point cloud,a density analysis method of 3D point cloud based on Delaunay triangulation is proposed.Firstly,project the point cloud onto a sphere surface,and then three dimensional Delaunay triangulation algorithm is used to process the spherical surface points,and finally the assemble of triangular areas is obtained to represent density analysis object on behalf of original point cloud.The comparison with existing method validate the performance.Simulation experiments are carried out to analyze the influence of parameters of the designed system,and appropriate configuration is finally determined.For the problem that point cloud data collected in single point of view has blind areas and the density of point cloud map is not high enough,a large-scale point cloud matching method based on improved NormalDistributions Transform(NDT)is put forward.The registration algorithm is used as front-end optimization part,and a complete graph of poses of different point cloud is generated in this stage.The complete graph is then optimized by the graph optimization algorithm.The complete high-density point cloud of large-scale scene is obtained according to the optimized graph.Experimental results show that the cloud consistency is good and point cloud density is very high.