Study On The 3D Laser Scanning Point Cloud Data Simplification And Modeling Application

The 3D laser scanning technology is an emerging mapping technology that integrates a variety of high and new technologies.Because it can improve the efficiency and accuracy of data collection and model reconstruction,it plays an important role in improving and enriching the geographic information database of underground space buildings(subway stations)and building “smart cities.”The 3D laser scanning technology can quickly collect the surface data of the target object.The obtained high-density point coordinate data is called point cloud data.Compared with traditional surveying and mapping data,point cloud data has a huge amount of data and a large amount of redundant data,which seriously affects the processing and calculation efficiency of point cloud data,and causes great inconvenience to the use and storage of point cloud data.Therefore,we study point cloud data segmentation and extraction and point cloud data simplification,which is of great significance for point cloud data application and 3D model reconstruction.This paper mainly studies the application of 3D laser scanning technology in 3D data acquisition and model reconstruction of subway stations,and studies the point cloud data segmentation and simplification for subway station point cloud data.The main contents and results of this paper are as follows:(1)In this paper,we take 3D laser scanning point cloud data collection as the research object of the subway station,and develop detailed operation procedures and methods for the problems existing in the 3D geographic information data collection of the subway station.We perform point cloud data registration on the collected point cloud data to obtain a complete 3D geographic information data of the subway station.We used 20 target points throughout the subway station to verify its accuracy,and the error of three coordinate directions is mot more than 0.02 m.It shows that the obtained3 D geographic information data of subway stations has high accuracy.(2)For the point cloud data processing software,the initial point cloud data was manually segmented and extracted.In order to improve the degree of automation,we used a cross number algorithm and made a point cloud data segmentation extraction tool based on VS2015 using the.Net framework.Compared with manual segmentation and extraction of point cloud data,it improves the degree of automation.(3)We have researched several commonly used point cloud data reduction methods.Aiming at the deficiency that the point cloud data simplification algorithmcannot retain the indicated area for point cloud data reduction,we propose an improved point cloud data reduction method based on RGB information.This method first uses RGB information to locate and retain the indicated identification area in point cloud data,then extracts and retains the contour information,and finally performs point cloud data reduction.We take the point cloud data of the pillars in the subway station as the experimental object.The experimental results show that the improved point cloud reduction algorithm is better than the commonly used point cloud data reduction methods.(4)For the reconstruction of indoor model of subway station,this paper uses manual selection of point cloud data and triangular mesh modeling to reconstruct the point cloud data model of subway station.The results show that the indoor model of the complete subway station reconstructed by manually selecting point cloud data is highly refined and meets the requirements of the geographic information database model,and can be applied to actual surveying and mapping production work.