A spatial index construction method and device for mass point cloud data

Abstract

The embodiments of the invention provide a spatial index construction method and device for mass point cloud data. The method comprises the steps of performing partitioning treatment on acquired original point cloud data to obtain a plurality of point cloud data blocks; for each point cloud data block, establishing an octree index of the current point cloud data block; performing combining treatment on the octree indexes of the multiple point cloud data blocks to obtain a spatial index structure of the original point cloud data. According to the technical solution, the conventional octree index structure is improved; partitioning treatment is performed on original point cloud data, so that mass point cloud data can be disassembled and thus memory space occupation in spatial index construction is reduced; after multiple octree indexes are combined, an octree index where target data are located can be found first and then target data can be searched for based on the found octree index in later query, so that the query efficiency is greatly increased.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of data processing, and in particular to a method and device for constructing a spatial index of massive point cloud data. Background technique [0002] Lidar scanning technology is an emerging 3D data acquisition technology. Lidar scanners mounted on different platforms such as tripods, cars, airplanes and satellites can quickly acquire massive point cloud data. Point cloud data contains rich information such as latitude and longitude coordinates, intensity, multiple echoes and colors of each point, and has related applications in the fields of surveying and mapping, forestry, agriculture and digital cities. Currently commonly used lidar scanners, such as Riegl, Faro, and Leica, can generate tens of thousands of points per second, and the number of data points acquired by each scan can reach hundreds of thousands or millions, and the amount of data can reach several Ten to hundreds of...

AI Technical Summary

Problems solved by technology

The grid index is easy to build and easy to encode, but it does not consider the spatial distribution of data when it is built, and it is not conducive to the rapid visualization of point cloud data; the quadtree index structure is simple, but it is difficult to determine when building a quadtree index for massive point clouds The number of points contained in the leaf nodes and the data distribution are uneven, the construction and query efficiency will be reduced; the KD tree index has advantages in data query and retrieval, but it takes a lot of time to establish the neighborhood relationship of points; the octree index is composed of four The fork tree index is extended to the three-dimensional space index structure in three-dimensional space. The construction process is relatively simple and has high index efficiency. However, for massive point cloud data, as the depth of the octree continues to increase, it will take up more Large memory space reduces query efficiency

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