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基于立体靶标的摄像机标定方法
引用本文:张捷,李新德,戴先中.基于立体靶标的摄像机标定方法[J].东南大学学报(自然科学版),2011(3):543-548.
作者姓名:张捷  李新德  戴先中
作者单位:东南大学自动化学院
基金项目:国家自然科学青年基金资助项目(60804063);江苏省自然科学基金资助项目(BK201022557);江苏省科技成果转化专项资金资助项目(BA2007058)
摘    要:为获取视觉测量系统中二维图像到三维空间位置的变换关系,提出一种基于立体靶标的摄像机标定方法.针对无畸变小孔成像模型,使用最小二乘法求解初始投影矩阵后通过LM准则对其优化;根据多张图像对应的投影矩阵,求解摄像机内参数及各相应外参数;引入二阶径向畸变模型,建立理想图像坐标和实际图像坐标间的方程求解初始畸变系数;使用LM准则...

关 键 词:摄像机标定  立体靶标  透镜畸变  投影矩阵  LM优化

Camera calibration method based on 3D board
g Jie,i Xinde,ai Xianzhong.Camera calibration method based on 3D board[J].Journal of Southeast University(Natural Science Edition),2011(3):543-548.
Authors:g Jie  i Xinde  ai Xianzhong
Affiliation:(School of Automation,Southeast University,Nanjing 210096,China)
Abstract:A method of camera calibration based on 3D calibration board,which is used for vision measuring system,is proposed in this paper.Transform matrix is worked out with least square method on an ideal pin-hole camera model and then optimized by Levenberg-Marquardt(LM) algorithm.According to several transform matrices,initial camera intrinsic parameter matrix and corresponding extrinsic parameter matrices are solved.Then,the distortion coefficient is estimated according to the relationship between the real image and the detected image coordinate with initial camera parameter fixed.At last,LM algorithm is applied to the global optimization and the more accurate intrinsic and extrinsic parameters are obtained.Experimental results indicate that error of equivalent focal length is less than 0.1% and error of principal point is less than 0.5 pixel when Gaussian noise with less than 0.5 pixel standard deviation is added to the simulation image data.Under the same Gaussian noise level the more the pictures used,the smaller the standard deviation of camera parameters.Compared with other calibration methods 3D calibration board proposed in this paper is more accurate.More stable camera parameters can be obtained by using more pictures of the calibration board.
Keywords:camera calibration  3D calibration board  lens distortion  transform matrix  Levenberg-Marquardt(LM) optimization
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