Camera calibration is a crucial step in robotics and computer vision. Accurate camera parameters are necessary to achieve robust applications. Nowadays, camera calibration process consists of adjusting a set of data to a pin-hole model, assuming that with a reprojection error close to cero, camera parameters are correct. Since all camera parameters are unknown, computed results are considered true. However, the pin-hole model does not represent the camera behavior accurately if the focus is considered. Real cameras change the focal length slightly to obtain sharp objects in the image and this feature skews the calibration result if a unique pin-hole model is computed with a constant focal length. In this paper, a deep analysis of the camera calibration process is done to detect and strengthen its weaknesses. The camera is mounted in a robot arm to known extrinsic camera parameters with accuracy and to be able to compare computed results with the true ones. Based on the bias that exist between computed results and the true ones, a modification of the widely accepted camera calibration method using images of a planar template is presented. A pin-hole model with distance dependent focal length is proposed to improve the calibration process substantially

翻译：相机标定是机器人和计算机视觉领域中的关键步骤。准确的相机参数对于实现稳健的应用至关重要。当前，相机标定过程通常通过将一组数据拟合到针孔模型，并假设重投影误差接近零时相机参数正确。由于所有相机参数未知，计算结果被视为真实值。然而，若考虑对焦因素，针孔模型无法准确表征相机的实际行为。真实相机会轻微改变焦距以获取图像中的清晰物体，若以恒定焦距计算单一针孔模型，这一特性会扭曲标定结果。本文对相机标定过程进行了深入分析，旨在检测并强化其薄弱环节。将相机安装在机器人手臂上，以精确已知外部相机参数并能够将计算结果与真实值进行比较。基于计算结果与真实值之间存在的偏差，本文提出了一种对广泛使用的平面模板图像标定方法的改进方案。为显著提升标定过程，本文提出了一种焦距随距离变化的针孔模型。