Researchon Data Processing Technologyof City Digital Modeling Point Cloud

The post-processing of city digital point cloud data is the core part of the whole city digital modeling project,and the post-processing of point cloud data needs to obtain high-precision point cloud data and practical and effective processing algorithm as backing.In view of this,this paper has determined the research content of post-processing technology of urban digital point cloud data.The accuracy of the acquired point cloud data is related to the success or failure of point cloud data postprocessing.Therefore,this paper firstly introduces the working principle,scanning measurement characteristics and data fusion technology of ground Lidar scanning system in detail.Secondly,the error theory is used to analyze the error sources and the influence of the error on the accuracy of the data,and the scheme to eliminate the error is given.Then,the focus of this paper is to research the post-processing technology of point cloud data of ground lidar.By analyzing and summarizing some existing post-processing algorithms and optimizing for their shortcomings,this paper presents Statistical Outlier Removal algorithm,an improved ICP mosaic algorithm,an improved segmentation algorithm based on region growing,and a point cloud patch formed after the segmentation of point cloud data is completed,an optimized nearest neighbor classification algorithm is proposed,The validity of the proposed improved algorithm is verified by the comparison simulation experiment.In order to test the practicability of the improved algorithm and find its shortcomings,this paper will optimize the point cloud data processing algorithm as a theoretical basis to open source PCL point cloud processing library for programming tools,using VC ++ MFC module to establish a three-dimensional laser point Cloud data processing and model display of the software system processing platform,the actual access to Changchun City,a barren district of the point cloud data processing and analysis.The experimental results show that the improved algorithm is effective for point cloud data processing in simple urban scenes.