基于点云数据的改进四叉树空间索引研究  

Research on Improved Quadtree Spatial Index Based on Point Cloud Data

作　　者：刘中华 李鹏飞[1] 许章平 LIU Zhonghua;LI Pengfei;XU Zhangping(College of Geomatics, Shandong University of Science and Technology, Qingdao 266590, China)

机构地区：[1]山东科技大学测绘科学与工程学院,山东青岛266590

出　　处：《测绘与空间地理信息》2019年第5期38-39,42,47,共4页Geomatics & Spatial Information Technology

基　　金：国家重大科学仪器设备开发专项--机载双频激光雷达产品开发和应用:机载雷达空间集成检校与数据精度评估(2013YQ120343)资助

摘　　要：随着测绘行业的不断发展,三维激光扫描技术已经成为地理信息产业中不可或缺的重要组成部分^([1]),然而点云庞大的数据量给处理带来了一定的困难。本文针对运用车载移动测量系统所获取的海量点云数据,采用了一种基于编码改进四叉树索引的点云数据组织处理方法。结果证明该方法提高了创建索引的速度,减少了树的深度以及数据的冗余量,并基于此编码进行最邻近查找,具有可行性和有效性。With the continuous development of the surveying and mapping industry, vehicle-borne mobile measurement technology has become an indispensable and important part of it. However, the huge data volume of point cloud data brings certain difficulties to processing. In this paper, a code-based improved quadtree index method is proposed to organize and manage massive point cloud data obtained by vehicle- borne mobile measurement system. Finally, it is proved that this method improves the speed of creating data indexes, reduces the depth of the tree and the amount of data redundancy. The nearest neighbor search based on this coding is feasible and effective.

关 键 词：海量点云 空间索引 四叉树 邻近查询 

分 类 号：P228[天文地球—大地测量学与测量工程]