基于局部曲面特征直方图的点云识别

doi:10.16451/j.cnki.issn1003-6059.202010008

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Abstract Aiming at fast recognition of 3D point clouds, a point cloud recognition algorithm based on local surface feature histogram is proposed. Firstly, the cyclic voxel filtering algorithm is applied to filter the point clouds with different resolutions to the specified resolution. Secondly, the points with obvious local characteristics are selected as the key points based on the key point search algorithm with the maximum mean curvature of the neighborhood. The feature descriptor of the key point is calculated according to the relationship between the center of gravity of the point clouds in the neighborhood and the normal and distance of each point in the neighborhood surface. Then, the features are matched according to the spatial relationship between the adjacent key points and the Euclidean distance of the feature descriptor. Finally, the multithread recognition framework is adopted to speed up the online recognition. The experimental results show that the recognition speed is high.

Key words： 3D Point Cloud Feature Descriptor Registration Recognition

Received: 22 June 2020

ZTFLH:

TP391.4

Corresponding Authors: LU Jun, Ph.D., professor. His research interests include computer vision and intelligent control.

About author:: HUA Bowen, master student. His research interests include computer vision.ZHU Bo, master student. His research interests include computer vision.

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Cite this article:

LU Jun,HUA Bowen,ZHU Bo. Point Cloud Recognition Based on Local Surface Feature Histogram[J]. , 2020, 33(10): 934-943.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202010008 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2020/V33/I10/934

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