一种新的摄像机一维标定方法

针对摄像机一维(1D)标定的问题, 将世界坐标系建立在1D标定物上, 提出新的数学模型, 给出了一种新的标定方法。一般地, 假设1D标定物与世界坐标系的X轴重合, 定义了1D标定点与其对应投影图像点之间的1D单应矩阵。从单幅视图出发, 推导了1D摄像机标定的基本约束方程。根据基本约束方程采用线性最小二乘估计摄像机的初值, 并以标定点的反投影误差最小为目标函数进行非线性优化得到最终的标定结果。通过仿真实验和真实实验证明了该算法的正确性和可行性。实验结果表明, 与传统的方法相比, 所提出的方法线性初值估计精度高, 且对于固定点不可见的情况, 无需估计固定点的图像投影坐标。

Abstract

Aiming at camera calibration with one dimensional (1D) objects, a new mathematical model of a novel method for camera calibration is proposed, in which the world coordinate system is established with the 1D object. Generally, assuming that the 1D calibration object is located along the X-axis of the world coordinate system and the 1D homography matrix between 1D calibration object and its image plane is defined. The basic constraint for 1D camera calibration from a single image is derived. The closed-form solution is estimated by linear least-square method based on the basic constraint equations and the final calibration results are obtained by minimizing the projection error of the points. Simulation results with real experiment show that the proposed method is valid and feasible. The experimental results indicate that compared with traditional method, the proposed novel method has the characteristic of higher closed-form solution precision and the image coordinates of the fixed point are not needed to be estimated when the fixed point is invisible.

参考文献

[1] Tsai R Y. A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off the-shelf TV cameras and lenses［J］. IEEE Journal of Robotics and Automation, 1987, 3(4): 323-344.

[2] 吕耀文, 王建立, 曹景太, 等. 抛物线运动点目标的单目测量［J］. 光学学报, 2013, 33(6): 0615001.

Lü Yaowen, Wang Jianli, Cao Jingtai, et al. Monocular measurement for point target with parabolic motion［J］. Acta Optica Sinica, 2013, 33(6): 0615001.

[3] 张跃强, 苏昂, 刘海波, 等. 基于多直线对应和加权最小二乘的位姿估计［J］. 光学精密工程, 2015, 23(6): 1722-1731.

Zhang Yueqiang, Su Ang, Liu Haibo, et al. Pose estimation based on multiple line hypothesis and iteratively reweighted least squares［J］. Optics and Precision Engineering, 2015, 23(6): 1722-1731.

[4] Oh H, Won D, Huh S, et al. Indoor UAV control using multi-camera visual feedback［J］. Journal of Intelligent & Robotic Systems, 2011, 61(1-4): 57-84.

[5] Zhang Z. A flexible new technique for camera calibration［J］. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2000, 22 (11): 1330-1334.

[6] Zhang Z Y. Camera calibration with one-dimensional objects［J］. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2004, 26(7): 892-899.

[7] Hartley R I, Zisserman A. Multiple view geometry in computer vision［M］. Second edtion. Cambridge: Cambridge University Press, 2004: 223-229, 104-109.

[8] Hammarstedt P, Sturm P, Heyden A. Degenerate cases and closed-form solutions for camera calibration with one-dimensional objects［C］. Proceedings of the Tenth IEEE International Conference on Computer Vision, 2005, 1: 317-324.

[9] Wu F C, Hu Z Y, Zhu H J. Camera calibration with moving one dimensional objects［J］. Pattern Recognition, 2005, 38(5): 755-765.

[10] Frana J A de, Stemmer M R, Frana M B de M, et al. Revisiting Zhang′s 1D calibration algorithm［J］. Pattern Recognition, 2010, 43(3): 1180-1187.

[11] Shi K, Dong Q, Wu F. Weighted similarity-invariant linear algorithm for camera calibration with rotating 1D objects［J］. IEEE Transactions on Image Processing, 2012, 21(8): 3806-3812.

[12] 史坤峰, 吴福朝. 相机一维标定的最优加权线性算法［J］. 计算辅助设计与图形学学报, 2014, 26(8): 1251-1257.

Shi Kunfeng, Wu Fuchao. Optimally weighted linear algorithm for camera calibration with 1D objects［J］. Journal of Computer-Aided Design & Computer Graphics, 2014, 26(8): 1251-1257.

[13] Qi F, Li Q, Luo Y, et al. Constraints on general motions for camera calibration with one dimensional objects［J］. Pattern Recognition, 2007, 40(6): 1785-1792.

[14] Frana J A de, Stemmer M R, Frana M B de M, et al. A new robust algorithmic for multi-camera calibration with a 1D object under general motions without prior knowledge of any camera intrinsic parameter［J］. Pattern Recognition, 2012, 45(10): 3636-3647.

[15] 付仲良, 周凡, 谢艳芳, 等. 基于像对基础矩阵的多像一维标定方法［J］. 光学学报, 2013, 33(6): 0615003.

Fu Zhongliang, Zhou Fan, Xie Yanfang, et al. One-dimensional multi-camera calibration based on fundamental matrix［J］. Acta Optica Sinica, 2013, 33(6): 0615003.

[16] He X, Zhang H, Hur N, et al. Complete camera calibration using line-shape objects［C］. Tencon 2006 Hong Kong IEEE Region 10 Conference, 2006: 1-4.

[17] 王波, 孙凤梅. 再论一维摄像机标定［J］. 计算辅助设计与图形学学报, 2014, 26(3): 452-456.

Wang Bo, Sun Fengmei. 1D camera calibration revisited［J］. Journal of Computer-Aided Design & Computer Graphics, 2014, 26(3): 452-456.

[18] 薛俊鹏, 苏显渝. 基于两个正交一维物体的单幅图像相机标定［J］. 光学学报, 2012, 32(1): 0115001.

Xue Junpeng, Su Xianyu. Camera calibration with single image based on two orthogonal one-dimensional objects［J］. Acta Optica Sinica, 2012, 32(1): 0115001.

[19] 洪洋, 孙秀霞, 蔡鸣, 等. 基于正交消隐点无穷单应的摄像机内参数自标定方法［J］. 中国激光, 2015, 42(12): 1208001.

Hong Yang, Sun Xiuxia, Cai Ming, et al. An intrinsic parameters self-calibration technique based on infinite homography between orthogonal vanishing points［J］. Chinese J Lasers, 2015, 42(12): 1208001.

[20] Triggs B. Auto calibration from planar scenes［C］. Proceedings of European Conference on Computer Vision, 1998, 1406: 89-105.

[21] Bouguet J Y. Camera calibration toolbox for Matlab［EB/OL］. (2015-10-14)［2016-05-20］. http://vision.caltech.edu/bouguetj/calib_doc/index.html.

吕耀文, 刘维, 徐熙平, 安喆. 一种新的摄像机一维标定方法[J]. 光学学报, 2016, 36(12): 1215005. Lü Yaowen, Liu Wei, Xu Xiping, An Zhe. A Novel Camera One-Dimensional Calibration Method[J]. Acta Optica Sinica, 2016, 36(12): 1215005.

一种新的摄像机一维标定方法

关于本站 Cookie 的使用提示

全站搜索

一种新的摄像机一维标定方法

相关论文

相关资讯

关于本站 Cookie 的使用提示

全站搜索