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：相机标定是计算机视觉应用中实现精确定量测量的关键过程。Zhang提出的主流方法依赖于使用多姿态下采集的平面标定点网格的大量图像。尽管Zhang方法灵活且易于实现，但仍存在一些局限性。对包括预定义畸变模型系数在内的整套参数进行同步优化，即使在重投影误差较小的情况下，仍可能导致图像边界处的畸变校正效果不佳或内参计算错误。事实上，涉及图像拼接（如多相机系统）的应用需要直至图像边缘区域的精确畸变映射。此外，内参会影响相机姿态估计的精度，这对于机器人导航中的视觉伺服和自动化装配等应用至关重要。本文提出一种通过单幅覆盖整个传感器的平面散斑图案图像来估计完整标定参数的方法。图像点与标定靶物理点之间的对应关系通过数字图像相关技术获得。在对主点进行先验评估后，分别计算有效焦距和外参。流程最终将在整个图像上获得稠密均匀的无模型畸变图。通过使用不同噪声水平的合成数据测试了所提方法的可行性，并将其计量性能与Zhang方法进行了比较。实际测试表明，所开发方法能够揭示多图像平均处理所掩盖的图像形成特性。