Organization And Management Of Airborne LiDAR Point-cloud Data Based On MongoDB

Airborne LiDAR is a kind of active earth observation system with a high degree of automation, scanning speed, and rarely influenced by weather conditions, etc. In addition, this system can obtain high precise, high dense three-dimensional terrain information directly. It is widely used in many fields such as digital city construction, topography mapping, coastal monitoring. With the emergence of new areas and the rapid development of new technologies, point-cloud data has changed a lot with the larger scale and uncertain data structure. The existing point-cloud data management softwares like Cyclone6.0, Polywork and Geomagic usually emphasis on the modeling and are lack of support for massive point-cloud data processing because of its inefficient organization and management of point-cloud data.To deal with these problems, this paper choose the massive unstructured airborne LiDAR point-cloud data as the research object, analyses its storage methods first and study on its organization and management. In this paper, the main research work and innovation are as follows:(1) Use MongoDB to manage point-cloud data. The past organization methods of point-cloud data based on the file or relational database storage are unable to deal with the storage and access of huge data, unstructured data management and expansibility, etc. This paper use MongoDB to store massive point-cloud data with uncertain data structure and excavate its property to guide the later study.(2) Design the multilayered spatial index of the massive point-cloud data based on MongoDB and it perform better than many other methods based on RAM. Firstly, split the point-cloud space twice with Hash code, then use B-tree index of MongoDB to arrange the second index code to generate the mix spatial index base on Hash code of 3DGrid and B-tree. Considering that area query efficiency always more important than point query efficiency in the application, use JSON to design the third index of point-cloud data to improve the area query efficiency and finally finish the multilayered spatial index based on MongoDB of the massive point-cloud data. Comparing to the traditional methods, this method is more efficiency.(3) Optimize the visualization performance of massive point-cloud data. On the basis of the massive point-cloud data organization and spatial index, this paper use data clipping, “5-5-5” dynamic schedule strategy, multithread and LOD technology to achieve visualization of massive point-cloud data using optimal scheduling approach with good continuity and smoothness in process of roaming.With the above obtained results in theory and practice, a visualization prototype system for massive airborne LiDAR point-cloud data is developed with MongoDB,OpenGL graphic library and C program language on the platform of Linux Ubuntu14.04. The effectiveness and superiority of the organization and management algorithms proposed in this paper are shown by the seamless running of the system developed.