Accurate segmentation method of ground point cloud based on plane fitting

被引：0

作者：

Wang C.-Y. ^{[1
,2
]}

Qiu W.-Q. ^{[2
]}

Liu X.-L. ^{[1
]}

Xiao B. ^{[3
]}

Shi C.-H. ^{[2
]}

机构：

[1] Xi'an Key Laboratory of Active Photoelectric Imaging Detection Technology, Xi'an Technological University, Xi'an

[2] College of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun

[3] School of Optoelectronic Engineering, Xi'an Technological University, Xi'an

来源：

Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) | 2023年 / 53卷 / 03期

关键词：

ground segmentation; plane fitting; point cloud; signal and information processing; unmanned driving;

D O I：

10.13229/j.cnki.jdxbgxb20221057

中图分类号：

O24 [计算数学];

学科分类号：

070102 ;

摘要：

Aiming at the problem that ground points in point cloud data can affect the precision and speed of environment perception，an accurate segmentation method of ground point cloud based on plane fitting was proposed. Firstly，the scene point cloud was divided into several areas based on the projection distance. Secondly，according to the average height in the area，the divided ground points and the normal vector direction of the ground plane，the ground plane fitting point was determined，and the ground plane was fitted. Finally，according the distance from the point to the ground plane to achieve ground segmentation. Using the KITTI dataset and the collected point cloud data to compare the proposed algorithm with four algorithms：RANSAC、GPF、R-GPF and PatchWork，verify the effectiveness of area division，fitting point screening and ground plane normal vector direction screening for ground segmentation. The experimental results show that after the area division，the far-distance sparse ground can be divided；after the fitting point screening，the ground segmentation accuracy reaches 0.9417 under the condition of low iteration. After screening the normal vector direction of the ground plane，fitting the wall to the ground was avoided. The proposed method is better than the four compared algorithms in terms of F1 score，recall rate and accuracy rate，and the speed can reach 42.78 Hz，which can divide the ground accurately and quickly. © 2023 Editorial Board of Jilin University. All rights reserved.

引用

页码：933 / 940

页数：7

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