• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

计算机工程与科学 ›› 2022, Vol. 44 ›› Issue (11): 1976-1984.

• 图形与图像 • 上一篇    下一篇

Tsai氏相机平面标定算法的一种解析改进

姚隆兴,韩江涛,张志毅   

  1. (西北农林科技大学信息工程学院,陕西 杨凌 712100)

  • 收稿日期:2021-03-28 修回日期:2021-07-25 接受日期:2022-11-25 出版日期:2022-11-25 发布日期:2022-11-25
  • 基金资助:
    国家自然科学基金(61702422);中央高校基本科研业务费(2452020172)

An analytical improvement of Tsai’s camera plane calibration algorithm

YAO Long-xing,HAN Jiang-tao,ZHANG Zhi-yi   

  1. (College of Information Engineering,Northwest A&F University,Yangling 712100,China) 
  • Received:2021-03-28 Revised:2021-07-25 Accepted:2022-11-25 Online:2022-11-25 Published:2022-11-25

摘要: 针对径向畸变的针孔透视投影模型,提出了一种简单快速的相机标定算法。该算法在假设图像中心点与CCD或CMOS传感器中心重合的前提下,将相机的内外参数和相机模型的畸变参数分离,以进一步进行线性相机标定,避免了非线性优化带来的误差,降低了算法复杂度,可适当提高标定精度,节约计算时间。首先,根据透视投影的交比不变性原理标定镜头的畸变系数;然后,根据旋转变换关系和平移变换关系,充分利用径向畸变约束、旋转变换的正交性和旋转矩阵特有的性质约束线性求解出相机的内参和外参;最后,通过实验与Tsai氏相机平面标定算法进行对比,所提算法在标定时间上节约了35%左右,在精度上至少提高了15%。

关键词: 相机标定, 交比不变, 比例因子, 线性解析

Abstract: Aiming at the pinhole perspective projection model of radial distortion, a simple and fast camera calibration algorithm is proposed. This algorithm separates the internal and external parameters of the camera and the distortion parameters of the camera model on the premise that the image center point coincides with the center of the CCD or CMOS sensor, so that the camera calibration can be further linearly performed, which avoids errors caused by nonlinear optimization, reduces the algorithm complexity, appropriately improves the calibration accuracy, and saves the calculation time. Firstly, the lens distortion coefficient is calibrated according to the principle of cross-ratio invariance of perspective projection. Then, according to the rotation transformation relationship and translation transformation relationship, the internal and external parameters of the camera are solved linearly by making full use of the radial distortion constraint, the orthogonality of the rotation transformation and the unique properties of the rotation matrix. Finally, experiments show that, compared with the Tsai’s camera plane calibration algorithm, this algorithm  saves about 35% in calibration time and improves accuracy by at least 15%.

Key words: camera calibration, constant cross ratio, scale factor, linear analysis