模式识别与人工智能
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模式识别与人工智能  2020, Vol. 33 Issue (10): 934-943    DOI: 10.16451/j.cnki.issn1003-6059.202010008
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基于局部曲面特征直方图的点云识别
陆军1, 华博文1, 朱波1
1.哈尔滨工程大学 智能科学与工程学院 哈尔滨 150001
Point Cloud Recognition Based on Local Surface Feature Histogram
LU Jun1, HUA Bowen1, ZHU Bo1
1.College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001

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摘要 针对三维点云的快速识别问题,文中提出基于局部曲面特征直方图的点云识别算法.首先,采用循环体素滤波算法,将不同分辨率的点云滤波至指定分辨率.再基于邻域曲率均值最大的关键点查找算法选取点云局部特征较明显的点作为关键点,根据关键点邻域内点云重心与邻域曲面内各点的法线和距离的关系计算关键点的特征描述符.然后,根据临近关键点间的空间关系和特征描述符欧氏距离进行特征匹配.最后,采用多线程识别框架,加快在线识别速度.实验表明文中算法识别速度较快.
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关键词 三维点云特征描述符配准识别    
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 words3D Point Cloud    Feature Descriptor    Registration    Recognition   
收稿日期: 2020-06-22     
ZTFLH: TP391.4  
通讯作者: 陆 军,博士,教授,主要研究方向为计算机视觉、智能控制.E-mail:lujun0260@sina.com.   
作者简介: 华博文,硕士研究生,主要研究方向为计算机视觉.E-mail:1041893894@qq.com.朱 波,硕士研究生,主要研究方向为计算机视觉.E-mail:2205252739@qq.com.
引用本文:   
陆军, 华博文, 朱波. 基于局部曲面特征直方图的点云识别[J]. 模式识别与人工智能, 2020, 33(10): 934-943. LU Jun, HUA Bowen, ZHU Bo. Point Cloud Recognition Based on Local Surface Feature Histogram. , 2020, 33(10): 934-943.
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