Point Cloud Data Management Based On Global Location Grid

With the development of technology,the ability to collect large-scale 3D point cloud data has been greatly enhanced,and technologies such as Li DAR,3D laser instrument scanning and aerial/aerospace image dense matching have produced a large amount of point cloud data,which has been widely used in topographic surveying,3D modelling,high-precision autonomous driving,agricultural governance and planning,archaeology and heritage conservation,medical and sports rehabilitation,etc.However,the efficient storage management and indexing of a large range of massive point cloud data has become one of the challenges faced by researchers and applicators.At present,the dataset is often used as the unit for organizing and managing the data of a single building or a small range of scenes,indexing based on the identification and location information of discrete points,which makes it difficult to support the integrated management of large site point clouds and to achieve the multi-grained object-oriented query and expression of the geographical entities reflected in the point clouds,and cannot meet the demand for refined modeling and visualization of complex objects in smart cities.GeoSOT global location grid framework uses hierarchical spatial cells as the management and indexing object,which can "pack" discrete points in a certain range into the corresponding location grid,which can avoid the shortcomings of discrete point management and indexing of point cloud data to a certain extent and help improve the efficiency of point cloud data storage and query retrieval.This paper aims to address the shortcomings of the current point cloud data organization and query indexing,research on the organization and management of point cloud data using GeoSOT global location grid,design and develop a prototype system for point cloud data management based on global location grid to realize point cloud data organization and management,query indexing and visual representation based on the global location grid framework,which can support the unified organization,efficient management,fast query and retrieval,global visual representation,and multi-source spatio-temporal information correlation analysis of large site point cloud data.The main research and innovation points of this paper are as follows:(1)The research analyzed the current research status of point cloud data organization and management and global location grid reference framework at home and abroad,analyzed their shortcomings and potential in unified storage,management,query and display of point cloud data,and explained the necessity of point cloud data organization and management based on global location grid.(2)Using GeoSOT-3D as a location framework,proposed a multi-level organization model for point cloud data based on a global location grid,designed a grid coding method for point cloud data to achieve multi-level,recursive grid modeling of point cloud datasets,point cloud segmentation objects,and discrete point data.(3)Designed indexing and query methods for point cloud data storage based on the global location grid,including point cloud data storage structure,grid-based point cloud data indexing methods,query methods,etc.(4)Designed and developed a prototype system for the integrated management of point cloud data based on the global location grid,realized the construction,storage,query indexing and visualization of a hierarchical grid model of point cloud data based on Cesium map engine,which initially verified the feasibility of the method proposed in this study.