Journal of Applied Science and Engineering

Published by Tamkang University Press


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Wenbin Xie This email address is being protected from spambots. You need JavaScript enabled to view it.1, Zhen Zhang1, Yuefei Wang1, Yuanyuan Zhang1, Liucun Zhu1

1Advanced Science and Technology Research Institute, Beibu Gulf University, Qinzhou  535011, China


Received: July 17, 2019
Accepted: January 5, 2020
Publication Date: June 1, 2020

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Using Fast Point Feature Histograms feature from point cloud for 3D object recognition or registration, Fast Point Feature Histograms feature descriptor is arbitrarily and inefficiently calculated by subjectively adjusting the neighborhood radius, and the whole process can’t be completed automatically. An adaptive neighborhood-selection Fast Point Feature Histograms point cloud feature extraction algorithm has proposed to solve this problem. Firstly, we estimate the point cloud density of many pairs of point clouds. Secondly, compute the neighborhood radius to extract the Fast Point Feature Histograms features for Sample Consensus Initial Alignment registration, and count the radius and the density when the registration performance is optimal, and then the cubic spline interpolation fitting is used to obtain the function expression of the radius and the density. Finally, the Fast Point Feature Histograms feature extraction algorithm has combined with the function to form adaptive neighborhood-selection Fast Point Feature Histograms feature extraction algorithm. The experimental results have shown that the proposed algorithm can adaptively choose the appropriate neighborhood radius according to the density of point cloud, and improve the Fast Point Feature Histograms feature matching performance. At the same time, it is improved the computing speed to a better value, which is of guiding significance.


Keywords: Fast Point Feature Histograms; Sample Consensus Initial Alignment; Point Cloud Density; Neighborhood Radius; Adaptive Selection



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