Self-adaptive road surface point cloud rapid removal method

An adaptive point cloud technology, applied in the field of image processing, can solve the problems of shortening ground removal time, long road surface removal time, and reduced number of iterations, so as to shorten ground removal time, improve time processing efficiency, and reduce iterations The effect of times

Pending Publication Date: 2022-05-20
BEIJING UNIV OF POSTS & TELECOMM
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Problems solved by technology

[0005] The present invention provides an adaptive road surface point cloud rapid removal method, which is used to solve the defects of long time-consuming and low time efficiency of road surface removal in the prior art, realizes reducing the number of iterations, shortening the ground removal time, and improving ground movement Time Processing Efficiency

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  • Self-adaptive road surface point cloud rapid removal method
  • Self-adaptive road surface point cloud rapid removal method
  • Self-adaptive road surface point cloud rapid removal method

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[0033] In order to make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the present invention. Obviously, the described embodiments are part of the embodiments of the present invention , but not all examples. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0034] The method for quickly removing adaptive road surface point clouds of the present invention will be described below in conjunction with the accompanying drawings. Please refer to figure 1 , the present invention provides a method for quickly removing adaptive road surface point clouds, including:

[0035] Step 10, obtaining point cloud data, and based on the point cloud data, adaptiv...

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Abstract

The invention provides a self-adaptive road surface point cloud rapid removal method, which comprises the following steps: acquiring point cloud data, and extracting bottom point cloud data based on the point cloud data; constructing a three-dimensional voxel grid for the bottom point cloud data, replacing the bottom point cloud data with the gravity center of each grid in the three-dimensional voxel grid, and obtaining new bottom point cloud data after replacing the bottom point cloud data; and based on an RANSAC algorithm, performing plane fitting on the new bottom point cloud data to obtain a road surface point cloud, and removing road surface points corresponding to the road surface point cloud. The method is used for solving the defects of poor ground segmentation effect and long consumed time in the prior art under the conditions that the proportion of ground points is too small and a plurality of road surface parameters exist in large-scene scattered point clouds, and the purposes of improving the removal effect, obtaining the plurality of road parameters, shortening the removal time and improving the time processing efficiency of ground removal are achieved.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a method for quickly removing adaptive road surface point clouds. Background technique [0002] When using lidar for road monitoring, the road surface information and target information are mixed in the point cloud data obtained. In order to separate ground points and non-ground points and improve the accuracy of subsequent target detection, the road ground point cloud must be extracted and removed. . Ground filtering technology is a key technology in point cloud processing, which plays an important role in obtaining road surface information, planning driving paths, and improving target detection accuracy. [0003] The commonly used ground removal techniques are mainly divided into three categories: grid-based methods, scan-line-based methods, and plane-fitting-based methods. Grid method: First, divide the original point cloud into different grid arrays according to ce...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/66G06T17/20
CPCG06T7/66G06T17/205G06T2207/10028
Inventor 左勇任阳杜志华宋晓菡伍剑洪小斌李岩郭宏翔邱吉芳
Owner BEIJING UNIV OF POSTS & TELECOMM
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