LiDAR point cloud filter method based on gradient and area growth

Abstract

The invention discloses a LiDAR point cloud filter method based on gradient and area growth. The method mainly includes the steps of firstly, acquiring the original LiDAR point cloud data; secondly, rejecting gross errors; thirdly, gridding rules; fourthly, selecting ground seed points; fifthly, performing area growth; sixthly, using the grids where the new ground points satisfying the conditions as centers and using the point, with the lowest elevation, in the grids as the new seed points for growing according to the fourth step until no points satisfy the conditions, and stopping growing; seventhly, sequentially performing area growth on seed points and centralized seed points according to the fourth step and the fifth step until the whole process is finished. By the method, the elevation threshold size is limited effectively, the calculation quantity of the whole method is lowered effectively, and the ground points and non-ground points in LiDAR point cloud data of complex cities can be separated fast. In addition, the method is simple, and the result obtained by the method is relatively small.

Description

technical field [0001] The invention belongs to the field of ground observation and relates to a LiDAR point cloud filtering method based on slope and region growth. Background technique [0002] Since the former US Vice President Al Gore put forward the concept of "Digital Earth" in 1998, it has aroused strong repercussions all over the world. The crystallization of the close integration of technology and social development and social science. As a part of the digital earth, the construction and application of digital cities has received more and more attention. The three-dimensional information of urban infrastructure has shown important application value in urban planning, municipal management, public safety and other aspects. Buildings are one of the most basic information in a digital city. The LIDAR system has the ability to directly describe the three-dimensional information on the surface of the survey area, and can quickly and efficiently collect the characteristic...

AI Technical Summary

Problems solved by technology

But this algorithm has some deficiencies: 1) For a point on a large-area object, such as a point on a large building, the greater the distance between it and the nearest ground point, the height difference threshold will vary with The distance increases and increases, that is, the conditions for rejecting it as a feature point are relaxed, which will make it impossible to filter out points on some large and relatively low ground features

2) The slope-based filtering algorithm judges the ground point by finding the adjacency relationship of the points and calculating the height difference between the two points point by point, which leads to a large amount of calculation and a slow speed of the algorithm

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