Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (3): 933-940.doi: 10.13229/j.cnki.jdxbgxb20221057

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Accurate segmentation method of ground point cloud based on plane fitting

Chun-yang WANG1,2(),Wen-qian QIU2,Xue-lian LIU1(),Bo XIAO3,Chun-hao SHI2   

  1. 1.Xi'an Key Laboratory of Active Photoelectric Imaging Detection Technology，Xi'an Technological University，Xi'an 710021，China
    2.College of Electronic and Information Engineering，Changchun University of Science and Technology，Changchun 130022，China
    3.School of Optoelectronic Engineering，Xi'an Technological University，Xi'an 710021，China
  • Received:2022-08-18 Online:2023-03-01 Published:2023-03-29
  • Contact: Xue-lian LIU E-mail:wangchunyang19@163.com;tearlxl@126.com

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Abstract:

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.

Key words: signal and information processing, point cloud, plane fitting, ground segmentation, unmanned driving

CLC Number: 

  • TN911.7

Fig.1

Flow of accurate ground point cloudsegmentation method based on plane fitting"

Fig.2

Point cloud data distribution and grid division"

Fig.3

Initial ground fit point"

Fig.4

Ground fitting point change map"

Fig.5

Schematic diagram of plane fitting"

Fig.6

Schematic diagram of the angle between actual normal vector and reference normal vector"

Fig.7

Schematic diagram of ground point selection"

Fig.8

Histogram of the number of point cloud and grids"

Fig.9

Ground segmentation overall map"

Fig.10

Ground segmentation detail map"

Fig.11

Maximum number of iterations is related to the accuracy of the segmentation"

Fig.12

Ground Segmentation partial map"

Fig.13

Experimental results map"

Table 1

Ground segmentation accuracy table"

算法精确率F1分数召回率准确率
RANSAC［12］0.93780.91640.89790.9149
GPF［13］0.96610.86780.79810.8828
R-GPF［14］0.90370.94230.98490.9395
PatchWork［15］0.92470.94620.96120.9462
本文算法0.93980.95460.98490.9553

Fig.14

Under-segmentation detail map"

Fig.15

Over-segmentation detail map"

Fig.16

Experimental results map of complex scenes"

Table 2

Ground segmentation running speed"

算法运行速度/Hz算法运行速度/Hz
RANSAC［12］14.24PatchWork［15］44.14
GPF［13］28.63本文42.78
R-GPF［14］34.47
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