改进的基于FPFH特征配准点云的方法

doi:10.3969/j.issn.1006-2475.2017.11.009

计算机与现代化

改进的基于FPFH特征配准点云的方法

(青岛科技大学信息科学技术学院，山东青岛 266061)

收稿日期:2017-02-20 出版日期:2017-11-21 发布日期:2017-11-21
作者简介:马大贺(1989-)，男，山东郓城人，青岛科技大学信息科学技术学院硕士研究生，研究方向：计算机视觉，图像处理; 刘国柱(1965-)，男，教授，硕士生导师，硕士，研究方向：网络安全。
基金资助:
山东省自然科学基金资助项目(ZR2014FM015)

Improved Method of Point Cloud Registration Based on FPFH Feature

(College of Information Science & Technology, Qingdao University of Science and Technology, Qingdao 266061, China)

Received:2017-02-20 Online:2017-11-21 Published:2017-11-21

摘要/Abstract

摘要： ICP(Iterative Closest Point)算法是点云配准中最常用的算法，而点云的FPFH(Fast Point Feature Histograms)特征可在点云配准中为其提供初始匹配信息。针对该方法的初始匹配中距离测度等问题，提出一种改进的基于FPFH特征配准点云的方法。点云配准时首先计算2个点云的点的FPFH特征之间的巴氏距离，以k-d树检索巴氏距离最小的对应点，然后利用奇异值分解计算初始转换矩阵，进行ICP算法精细匹配，求得最终变换矩阵。实验结果表明，改进的基于FPFH特征配准点云的方法能为ICP算法提供良好的初始变换矩阵，在同等迭代次数下该方法具有更高的精度。

关键词: 巴氏距离, FPFH, 点云特征, 点云配准

Abstract: ICP algorithm is the most commonly used algorithm in point cloud registration, and the FPFH feature can provide initial matching information to register point cloud. An improved method based on FPFH feature registration of point cloud is proposed. Firstly, the Bhattacharyya distance between the FPFH features of two point clouds is calculated. The k-d tree is used to retrieve the corresponding points with the smallest Bhattacharyya distance. Then, the initial transformation matrix is calculated, the ICP algorithm gets the final transformation matrix. Experiment shows that the method has higher accuracy under the same iterations.

Key words: Bhattacharyya distance, FPFH, point cloud feature, point cloud registration

中图分类号:

TP391.41

马大贺，刘国柱. 改进的基于FPFH特征配准点云的方法[J]. 计算机与现代化, doi: 10.3969/j.issn.1006-2475.2017.11.009.

MA Da-he, LIU Guo-zhu. Improved Method of Point Cloud Registration Based on FPFH Feature[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2017.11.009.

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改进的基于FPFH特征配准点云的方法

Improved Method of Point Cloud Registration Based on FPFH Feature

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