Camera calibration is a process of paramount importance in computer vision applications that require accurate quantitative measurements. The popular method developed by Zhang relies on the use of a large number of images of a planar grid of fiducial points captured in multiple poses. Although flexible and easy to implement, Zhang's method has some limitations. The simultaneous optimization of the entire parameter set, including the coefficients of a predefined distortion model, may result in poor distortion correction at the image boundaries or in miscalculation of the intrinsic parameters, even with a reasonably small reprojection error. Indeed, applications involving image stitching (e.g. multi-camera systems) require accurate mapping of distortion up to the outermost regions of the image. Moreover, intrinsic parameters affect the accuracy of camera pose estimation, which is fundamental for applications such as vision servoing in robot navigation and automated assembly. This paper proposes a method for estimating the complete set of calibration parameters from a single image of a planar speckle pattern covering the entire sensor. The correspondence between image points and physical points on the calibration target is obtained using Digital Image Correlation. The effective focal length and the extrinsic parameters are calculated separately after a prior evaluation of the principal point. At the end of the procedure, a dense and uniform model-free distortion map is obtained over the entire image. Synthetic data with different noise levels were used to test the feasibility of the proposed method and to compare its metrological performance with Zhang's method. Real-world tests demonstrate the potential of the developed method to reveal aspects of the image formation that are hidden by averaging over multiple images.

翻译：相机标定是在需要精确定量测量的计算机视觉应用中至关重要的一项技术。张正友提出的经典方法依赖于使用多幅不同姿态下拍摄的平面网格标记点图像。尽管该方法灵活且易于实现，但仍存在一些局限性。当同时优化包括预定义畸变模型系数在内的全部参数集时，即使重投影误差控制得相当小，也可能导致图像边缘区域的畸变校正效果不佳，或内参计算出现偏差。事实上，涉及图像拼接（如多相机系统）的应用需要精确映射直至图像最外缘区域的畸变。此外，内参参数直接影响相机位姿估计的精度，而位姿估计是机器人导航中视觉伺服控制和自动化装配等应用的基础。本文提出一种方法，通过单幅覆盖整个传感器的平面散斑图案图像，即可估计完整的标定参数集。利用数字图像相关技术建立图像点与标定靶标上物理点之间的对应关系。在预先估计主点后，分别计算有效焦距和外参。最终在全图像范围内获得密集且均匀的无模型畸变映射。研究采用不同噪声水平的合成数据验证所提方法的可行性，并将其计量性能与张氏方法进行对比。实景测试表明，该方法能够揭示在多幅图像平均处理中被掩盖的图像形成特性。