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本文分析当前索引方法存在问题,针对高效海量点云数据的要求,提出一种基于Hilbert码与R树的二级索引方法。论文阐述了二级索引的建立原理与方法,可通过聚类方法与R树度M值来的优化第一级索引;使用Hilbert R树作为第二索引,可以有效控制两级R树的高度,同时点云的增加与更新可只在局部进行。最后本文通过两组实验来验证该数据组织方法的可行性和跟其他索引(KD树与四叉树)进行比较,得出它是一种高效管理海量点云的方法。
This paper analyzes the existing problems of the indexing method. In order to meet the requirements of efficient massively point cloud data, a two-level indexing method based on Hilbert code and R-tree is proposed. The paper describes the establishment of two-level index of the principle and method, through the clustering method and R tree M value to optimize the first level index; using Hilbert R tree as the second index, you can effectively control two-level R tree height, At the same time point cloud increase and update can only be carried out locally. Finally, we verify the feasibility of the data organization method by two sets of experiments and compare it with other indexes (KD-tree and quadtree) and conclude that it is a method to efficiently manage massive point cloud.