A Fast Ground Segmentation Method for 3D Point Cloud


Phuong Chu, Seoungjae Cho, Sungdae Sim, Kiho Kwak, Kyungeun Cho, Journal of Information Processing Systems Vol. 13, No. 3, pp. 491-499, Jun. 2017  

10.3745/JIPS.02.0061
Keywords: 3D point cloud, Ground Segmentation, Light Detection and Ranging, Start-Ground Point, Threshold Point
Fulltext:

Abstract

In this study, we proposed a new approach to segment ground and nonground points gained from a 3D laser range sensor. The primary aim of this research was to provide a fast and effective method for ground segmentation. In each frame, we divide the point cloud into small groups. All threshold points and start- ground points in each group are then analyzed. To determine threshold points we depend on three features: gradient, lost threshold points, and abnormalities in the distance between the sensor and a particular threshold point. After a threshold point is determined, a start-ground point is then identified by considering the height difference between two consecutive points. All points from a start-ground point to the next threshold point are ground points. Other points are nonground. This process is then repeated until all points are labelled


Statistics
Show / Hide Statistics

Statistics (Cumulative Counts from November 1st, 2017)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.




Cite this article
[APA Style]
Phuong Chu, Seoungjae Cho, Sungdae Sim, Kiho Kwak, & Kyungeun Cho (2017). A Fast Ground Segmentation Method for 3D Point Cloud. Journal of Information Processing Systems, 13(3), 491-499. DOI: 10.3745/JIPS.02.0061.

[IEEE Style]
P. Chu, S. Cho, S. Sim, K. Kwak and K. Cho, "A Fast Ground Segmentation Method for 3D Point Cloud," Journal of Information Processing Systems, vol. 13, no. 3, pp. 491-499, 2017. DOI: 10.3745/JIPS.02.0061.

[ACM Style]
Phuong Chu, Seoungjae Cho, Sungdae Sim, Kiho Kwak, and Kyungeun Cho. 2017. A Fast Ground Segmentation Method for 3D Point Cloud. Journal of Information Processing Systems, 13, 3, (2017), 491-499. DOI: 10.3745/JIPS.02.0061.