Mass airborne LiDAR point cloud Delaunay triangulation network parallel construction method and apparatus thereof

Abstract

The invention relates to a mass airborne LiDAR point cloud Delaunay triangulation network parallel construction method. The method comprises the following steps: carrying out data block division and task distribution on mass airborne LiDAR point cloud data; applying a pointwise insertion algorithm to each data block respectively so as to construct a sub-triangulation network in parallel, and acquiring a sub-triangulation network set; using a merging algorithm to connect the adjacent two sub-triangulation networks so as to generate the merged triangulation network; using an optimization algorithm to carry out optimization on a newly generated triangle and the adjacent triangle so as to obtain the merged triangle which accords with a Delaunay rule; repeating the merging and the optimization until all the sub-triangulation networks are merged and the complete Delaunay triangulation network can be obtained. The invention also provides a corresponding apparatus for executing the above method. By using the method and the apparatus of the invention, a post-treatment speed of the mass airborne LiDAR point cloud can be substantially increased. The method and the apparatus of the invention have great significance for practical application.

Description

technical field [0001] The present invention relates to the technical field of surveying and mapping, and more specifically, to a method and device for parallel construction of massive airborne LiDAR point cloud Delaunay triangulation. Background technique [0002] LiDAR has been widely used in the field of terrain surveying and mapping. Using airborne LiDAR for terrain scanning can obtain massive airborne LiDAR point cloud data. Based on the high-precision three-dimensional spatial coordinate information contained in the point cloud data, it can accurately express the surface spatial characteristics of the scanned object, which is the basis for various surveying and mapping applications, such as geospatial information research, urban planning, land management, etc. aspect is also important. However, LiDAR point cloud data usually has a large number of points, and the spatial distribution of each point has a certain degree of discreteness, so a scientific and efficient met...

AI Technical Summary

Problems solved by technology

When the number of point clouds is large, the calculation of Delaunay triangulation in the process of constructing Delaunay triangulation is large, and the existing algorithm is very slow in execution speed and low in efficiency, which can no longer meet the current application of massive data network construction.

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