Application of Improved DBSCAN Clustering Method in Point Cloud Data Segmentation

被引：6

作者：

Wang, Chunxiao ^{[1
]}

Xiong, Xiaoqing ^{[1
]}

Yang, Houqun ^{[2
]}

Liu, Xiaojuan ^{[1
]}

Liu, Lu ^{[1
]}

Sun, Shihao ^{[1
]}

机构：

[1] Minist Nat Resources, Hainan Geomet Ctr, Haikou, Hainan, Peoples R China

[2] Hainan Univ, Sch Comp Sci & Technol, Haikou, Hainan, Peoples R China

来源：

2021 2ND INTERNATIONAL CONFERENCE ON BIG DATA & ARTIFICIAL INTELLIGENCE & SOFTWARE ENGINEERING (ICBASE 2021) | 2021年

关键词：

LiDAR; point cloud; segmentation; density clustering; DBSCAN;

D O I：

10.1109/ICBASE53849.2021.00034

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Point cloud data segmentation is the main task of point cloud data processing and it is also the prerequisite and key link for realizing automatic recognition of features. A laser point cloud data segmentation method based on the density clustering method is proposed in this paper and is verified in airborne cloud data segmentation. The method is an unsupervised clustering technique and the experiments show that this method has the advantages o multi-dimensional, good segmentation effect and high robustness.

引用

页码：140 / 144

页数：5

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