基于Hilbert排列码与R树的海量LIDAR点云索引

A Hilbert code and R-tree organizing method for large point clouds from LiDAR

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。

Aiming at large point-clouds data, a novel 2-1eve1 index structure was proposed based on Hilbert code and R-tree. Hilbert space-filling curve was introduced to cluster LiDAR data point group and data volume of each group was controlled under the desired size, which improve the spatial cluster grouping and R-tree insertion algorithms, and therefore evidently reduce the overlap of Rtree sibling nodes and even the size of nodes. Using real data for test, the new method is proved having superior performances in several aspects.