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Open AccessArticle
The Improvement of Density Peaks Clustering Algorithm and Its Application to Point Cloud Segmentation of LiDAR
by
Zheng Wang
Zheng Wang ,
Xintong Fang
Xintong Fang ,
Yandan Jiang
Yandan Jiang
,
Haifeng Ji
Haifeng Ji ,
Baoliang Wang
Baoliang Wang
and
Zhiyao Huang
Zhiyao Huang *
State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China
*
Author to whom correspondence should be addressed.
Sensors 2024, 24(17), 5693; https://doi.org/10.3390/s24175693 (registering DOI)
Submission received: 26 July 2024
/
Revised: 25 August 2024
/
Accepted: 30 August 2024
/
Published: 1 September 2024
Abstract
This work focuses on the improvement of the density peaks clustering (DPC) algorithm and its application to point cloud segmentation in LiDAR. The improvement of DPC focuses on avoiding the manual determination of the cut-off distance and the manual selection of cluster centers. And the clustering process of the improved DPC is automatic without manual intervention. The cut-off distance is avoided by forming a voxel structure and using the number of points in the voxel as the local density of the voxel. The automatic selection of cluster centers is realized by selecting the voxels whose gamma values are greater than the gamma value of the inflection point of the fitted curve as cluster centers. Finally, a new merging strategy is introduced to overcome the over-segmentation problem and obtain the final clustering result. To verify the effectiveness of the improved DPC, experiments on point cloud segmentation of LiDAR under different scenes were conducted. The basic DPC, K-means, and DBSCAN were introduced for comparison. The experimental results showed that the improved DPC is effective and its application to point cloud segmentation of LiDAR is successful. Compared with the basic DPC, K-means, the improved DPC has better clustering accuracy. And, compared with DBSCAN, the improved DPC has comparable or slightly better clustering accuracy without nontrivial parameters.
Share and Cite
MDPI and ACS Style
Wang, Z.; Fang, X.; Jiang, Y.; Ji, H.; Wang, B.; Huang, Z.
The Improvement of Density Peaks Clustering Algorithm and Its Application to Point Cloud Segmentation of LiDAR. Sensors 2024, 24, 5693.
https://doi.org/10.3390/s24175693
AMA Style
Wang Z, Fang X, Jiang Y, Ji H, Wang B, Huang Z.
The Improvement of Density Peaks Clustering Algorithm and Its Application to Point Cloud Segmentation of LiDAR. Sensors. 2024; 24(17):5693.
https://doi.org/10.3390/s24175693
Chicago/Turabian Style
Wang, Zheng, Xintong Fang, Yandan Jiang, Haifeng Ji, Baoliang Wang, and Zhiyao Huang.
2024. "The Improvement of Density Peaks Clustering Algorithm and Its Application to Point Cloud Segmentation of LiDAR" Sensors 24, no. 17: 5693.
https://doi.org/10.3390/s24175693
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