简易高精度的平面五点摄像机标定方法

邹建成; 田楠楠

doi:10.3788/OPE.20172503.0786

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简易高精度的平面五点摄像机标定方法

信息科学 | 更新时间：2020-07-07

- 简易高精度的平面五点摄像机标定方法
- An easy and high-precision camera calibration method based on planar five points
- 光学精密工程 2017年25卷第3期页码：786-791
- 作者机构：
  
  北方工业大学图像处理与模式识别研究所, 北京 100144
- 作者简介：
  
  [ "邹建成 (1966-), 男, 博士, 教授, 博士生导师, 1996年毕业于中国科学院数学研究所, 研究方向为计算机视觉和多媒体信息处理, E-mail:zjc@ncut.edu.cn" ]
  田楠楠 (1992-), 女, 硕士研究生, 研究方向为计算机视觉。E-mail:tian-nan-nan@qq.com TIAN Nan-nan, E-mail:tian-nan-nan@qq.com
- 基金信息：
  
  国家科技重大专项课题(2014ZX02502003);国家自然科学基金资助项目(61170327)
- DOI：10.3788/OPE.20172503.0786
  中图分类号： TP391.41
- 收稿日期：2016-12-02，
  
  录用日期：2017-1-14，
  
  纸质出版日期：2017-03-25
- 稿件说明：
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邹建成, 田楠楠. 简易高精度的平面五点摄像机标定方法[J]. 光学精密工程, 2017,25(3):786-791.

Jian-cheng ZOU, Nan-nan TIAN. An easy and high-precision camera calibration method based on planar five points[J]. Optics and precision engineering, 2017, 25(3): 786-791.
邹建成, 田楠楠. 简易高精度的平面五点摄像机标定方法[J]. 光学精密工程, 2017,25(3):786-791. DOI： 10.3788/OPE.20172503.0786.

Jian-cheng ZOU, Nan-nan TIAN. An easy and high-precision camera calibration method based on planar five points[J]. Optics and precision engineering, 2017, 25(3): 786-791. DOI： 10.3788/OPE.20172503.0786.

摘要

为了进一步提高相机参数精度，简化标定过程，在张正友平面标定算法的基础上，提出了一种较简单的5点标定算法。本文利用一个矩形作为标定模板，将其4个角点和中心点作为待匹配点。仅利用不同角度拍摄的10（10~20幅均可）幅图像，即可求解全部相机内外参数。首先，为了减小数据对结果的影响，对获取的5个点的图像坐标进行归一化，将点坐标控制在以原点为中心，

http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=1754193&type=

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http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=1754193&type=middle

为半径的圆内；其次，相机成像模型未考虑畸变，考虑到单应矩阵可能会有零元素，故按照9个自由度来求解；再次，求解出相机的内外参数，将求得结果的平均值作为非零因子

的值；最后利用Levenberg-Marquarat（LM）算法对所有内外参数进行整体优化。选用不同噪声水平的模拟实验和真实图像进行测试，结果显示该算法比张正友棋盘格算法的精度要高。

Abstract

A simpler 5-point calibration algorithm was proposed to further improve the accuracy of camera parameter and simplify the calibration process

on the basis of Zhang Zhengyou's plane calibration algorithm. In this method

a rectangle was used to serve as a calibration template and the four angular points and central point were selected as the points to be matched. All internal and external parameters could be solved with only 10 (10-20 all were ok) images shot in different angles. First

to decrease the influence of data on result

normalization shall be carried out for image coordinates of five points obtained

where the coordinate of point shall be controlled within the circle with the origin as the center and

http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=1754197&type=

http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=1754197&type=small

http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=1754197&type=middle

as the radius; then

because it didn't consider distortion in imaging model of camera but considered that there might have been zero element in homography

caculation was solved in accordance with 9 degrees of freedom; again

the internal and external parameters of camera were caculated and the value of non-zero divisor

was the mean of result acquired; finally

global optimization for internal and external parameters was carried out with Levenberg-Marquarat (LM) algorithm. Through simulation test with different levels of noise and test of true image

the accuracy of this algorithm is higher than Zhang Zhengyou's checkerboard algorithm.

关键词

Keywords

references

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