Recognition of key targets of locomotive bottom based on 3D point cloud data

被引：0
|
作者：
Huang, Qian [1 ,2 ]
Wang, Ze-yong [1 ]
Li, Jin-long [1 ]
Jiang, Wen-nan [1 ]
Gao, Xiao-rong [1 ]
机构：
[1] Southwest Jiaotong Univ, Sch Phys Sci & Technol, Chengdu 610031, Sichuan, Peoples R China
[2] Southwest Jiaotong Univ, Chengdu 610031, Sichuan, Peoples R China
来源：
PROCEEDINGS OF 2017 IEEE FAR EAST FORUM ON NONDESTRUCTIVE EVALUATION/TESTING: NEW TECHNOLOGY & APPLICATION (IEEE FENDT 2017) | 2017年
关键词：
3D point cloud; ISS; FPFH; Feature recognition; REGISTRATION;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
In this paper, a new method is designed to recognize the key targets of the locomotive bottom based on the 3D point cloud data. In this method, the key points were selected on the basis of ISS (Intrinsic Shape Signatures) algorithm, the feature descriptors were constructed using FPFH (Fast Point Feature Histograms) algorithm, and the recognition and localization of the targets were conducted by the combination of template matching and cluster analysis. The experiment was carried out on bolts for validation. Experimental results verified the effectiveness of the proposed method and proved the feasibility of recognition and localization of key targets of the locomotive bottom based on 3D point cloud data.
引用
收藏
页码：149 / 153
页数：5
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