Point cloud data automatic filtering method based on grid segmentation and moving least square

Abstract

Provided is a point cloud data automatic filtering method based on grid segmentation and moving least squares, including the following steps: performing segmentation processing on laser point cloud data to obtain a plurality of grids; adopting a dynamic quadtree method to establish corresponding indexes for each grid; determining whether the density of the laser point cloud data in the grids is larger than a density threshold, and adopting rarefying processing on the grids whose laser point cloud data density is greater than the density threshold; adopting moving least squares on the laser point cloud data in all grids to fit a digital elevation model which is used as a reference surface; calculating respective distance of each laser point in all the grids to the reference surface, and deleting the laser points whose distance to the reference surface is greater than a distance threshold, the remaining laser point cloud data being kept; and repeatedly performing the above steps on the kept laser point cloud data until the distance of the laser points in all the grids to the reference surface is smaller or equal to a current distance threshold.

Description

technical field [0001] The invention belongs to the field of massive point cloud data processing, and in particular relates to a fully automatic filtering method based on grid division and moving least squares. Background technique [0002] Since the laser can obtain the three-dimensional coordinate information of ground objects in a short time, and the amount of data is huge, how to quickly extract useful information from massive laser point cloud data is a hot spot and difficulty in current research. Particularly important. Many literatures at home and abroad have discussed and studied point cloud filtering, and proposed many filtering algorithms, including filtering algorithms based on mathematical morphology, filtering algorithms based on slope, progressive encryption algorithms based on TIN and filtering algorithms based on data segmentation. Wait, some research results have also been obtained, and there are still some problems that have not been resolved, such as the ...

AI Technical Summary

Problems solved by technology

The disadvantage of this algorithm is that it is difficult to find a suitable training sample area to obtain a filter function to deal with various other situations

[0004] Due to the large amount of laser point cloud data, and the irregular, scattered, and complex nature of point cloud data determines the complexity and difficulty of point cloud data processing.

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