张蕊, 李广云, 王力, 李明磊, 周阳林. 车载LiDAR点云混合索引新方法[J]. 武汉大学学报 ( 信息科学版), 2018, 43(7): 993-999. DOI: 10.13203/j.whugis20160441
引用本文: 张蕊, 李广云, 王力, 李明磊, 周阳林. 车载LiDAR点云混合索引新方法[J]. 武汉大学学报 ( 信息科学版), 2018, 43(7): 993-999. DOI: 10.13203/j.whugis20160441
ZHANG Rui, LI Guangyun, WANG Li, LI Minglei, ZHOU Yanglin. A New Method of Hybrid Index for Mobile LiDAR Point Cloud Data[J]. Geomatics and Information Science of Wuhan University, 2018, 43(7): 993-999. DOI: 10.13203/j.whugis20160441
Citation: ZHANG Rui, LI Guangyun, WANG Li, LI Minglei, ZHOU Yanglin. A New Method of Hybrid Index for Mobile LiDAR Point Cloud Data[J]. Geomatics and Information Science of Wuhan University, 2018, 43(7): 993-999. DOI: 10.13203/j.whugis20160441

车载LiDAR点云混合索引新方法

A New Method of Hybrid Index for Mobile LiDAR Point Cloud Data

  • 摘要: 以车载LiDAR点云数据为研究对象,为提高点云数据的组织与管理效率,提出了一种全局KD树与局部八叉树相结合的混合空间索引结构—KD-OcTree。全局KD树通过分辨器、分割平面的确定,重构点云之间的邻域关系,确保索引结构的整体平衡; 在其叶子节点再构造二级索引结构—局部八叉树,避免了单一八叉树结构点云分布不均衡、树结构深度过大、出现大量无点空间等现象。以3个真实场景数据为测试数据进行试验和对比分析,结果表明,KD-OcTree混合索引不仅能够提高索引构建、邻域搜索的速度,还对分类可靠性产生一定影响。

     

    Abstract: For mobile LiDAR point cloud data, a new hybrid index structure combining global KD-tree and local Octree is proposed to improve the efficiency of data organization and management, which is named as KD-OcTree index. Firstly, global KD-tree reconstructs the spatial neighborhood relations by defining the segmenting dimension and segmenting planes, for the purpose of ensuring the balance of the whole index. Then, local Octree is constructed in the leafs of KD-tree, which can avoid some shortcomings such as the unbalance of point cloud distribution, deeper Octree, large amount of non-point space, and so on. Lastly, we take three real scenes' point clouds as test data to process. The experimental results and comparative analysis show that the KD-OcTree index can not only improve the speed of constructing index and neighborhood searching, but also improve the effect of data-processing and influence the reliability of classification.

     

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