融合高斯核及指数函数聚类的点云目标物提取

doi:10.3969/j.issn.0255-8297.2022.03.005

Abstract

Abstract: In view of problems of repeatability of cluster center and disability of conducting point cloud clustering, a point cloud clustering method of clustering center homogenization combining Gaussian kernel and exponential function is proposed to optimize homogenization distribution of cluster centers and achieve the homogeneous clustering of point cloud. Firstly, local density is determined according to the Gaussian kernel function and density exponential function, and distance parameters are determined according to the size of local density. Then cluster centers are determined according to the product of the local density and distance parameters, and the proximity of the cluster centers is eliminated, so that the cluster centers are more evenly distributed in the entire data set. Finally, the distances between the data point and the cluster centers are used to determine the cluster attribution of each data, and the neighboring clusters are combined to achieve the extraction of the point cloud target. This algorithm is compared with the clustering function based on density peak (CFDP), K-means clustering algorithm, DBSCAN (density-based spatial clustering of applications with noise) algorithm, and the advantages of clustering algorithm in this paper are confirmed. Compared with the DPC algorithm and the deep learning method, the accuracy of objects point cloud extraction with different resolutions is 96.7%. The proposed method is prior than the other two methods in terms of computational efficiency and precision.

Key words: object extraction, classify, segmentation, density-based clustering

CLC Number:

TP201

CHEN Xijiang, AN Qing, BAN Ya, WANG Dexin, LI Kun, LIU Haipeng. Point Cloud Object Extraction Based on Gaussian Kernel Function and Exponential Function Clustering[J]. Journal of Applied Sciences, 2022, 40(3): 411-422.

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Point Cloud Object Extraction Based on Gaussian Kernel Function and Exponential Function Clustering

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