基于二维图像特征的点云配准方法

doi:10.13474/j.cnki.11-2246.2017.0313

测绘通报 ›› 2017, Vol. 0 ›› Issue (10): 39-42,73.doi: 10.13474/j.cnki.11-2246.2017.0313

基于二维图像特征的点云配准方法

赵夫群^1,2, 周明全^2,3

1. 咸阳师范学院教育科学学院, 陕西咸阳 712000;
2. 西北大学信息科学与技术学院, 陕西西安 710127;
3. 北京师范大学信息科学与技术学院, 北京 100875

收稿日期:2017-03-10 出版日期:2017-10-25 发布日期:2017-11-07
作者简介:赵夫群(1982-),女,博士生,讲师,主要研究方向为图形图像处理。E-mail:fuqunzhao@126.com
基金资助:
国家自然科学基金（61373117）

Point Cloud Registration Method Based on 2D Image Feature

ZHAO Fuqun^1,2, ZHOU Mingquan^2,3

1. School of Education Science, Xianyang Normal University, Xianyang 712000, China;
2. Shool of Information Science and Technology, Northwest University, Xi'an 710127, China;
3. School of Information Science and Technology, Beijing Normal University, Beijing 100875, China

Received:2017-03-10 Online:2017-10-25 Published:2017-11-07

摘要/Abstract

摘要： 为了提高低覆盖率点云的配准精度和收敛速度，提出了一种基于二维图像特征的点云配准方法。首先采用基于区域层次的点云配准算法实现粗配准；然后将三维点云转换成二维图像，再采用SURF算法提取二维图像的特征，并求解其匹配像素点对；最后根据二维匹配点获取相应的三维点云相关点，并计算刚体变换，由此实现点云的快速精确配准。试验结果表明，与迭代最近点（ICP）算法相比，该点云配准方法的配准精度和耗时分别提高了约20%和60%，是一种快速、高精度的点云配准算法。

关键词: 点云配准, 图像特征, 区域层次, 旋转矩阵, 迭代最近点

Abstract: A point cloud registration method is proposed in the paper in order to improve the registration accuracy and convergence rate of low overlapping point clouds.Firstly,a point cloud registration algorithm based on region level is used to complete coarse registration;Secondly,3D point cloud is converted into 2D image,SURF(speeded up robust features)algorithm is used to extract 2D image features,and the matching pixel pairs are solved;Finally,3D corresponding points are gotten according to the 2D matching pixels,and the rigid transformation is solved,then the fast and accurate registration of point cloud are achieved.The experimental results showed that the registration accuracy and convergence rate of proposed point cloud registration method improved about 20% and 60% respectively compared with iterative closest point(ICP)algorithm,it is a high accurate and fast point cloud registration method.

Key words: point cloud registration, image feature, region level, rotation matrix, iterative closest point

中图分类号:

赵夫群, 周明全. 基于二维图像特征的点云配准方法[J]. 测绘通报, 2017, 0(10): 39-42,73.

ZHAO Fuqun, ZHOU Mingquan. Point Cloud Registration Method Based on 2D Image Feature[J]. 测绘通报, 2017, 0(10): 39-42,73.

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基于二维图像特征的点云配准方法

Point Cloud Registration Method Based on 2D Image Feature

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