自动对焦镜头在相机标定过程中的影响
The Influence of Autofocus Lenses in the Camera Calibration Process
AI总结：
本文针对相机标定中自动对焦导致焦距变化的问题，提出距离相关焦距的针孔模型改进标定方法，并通过机器人手臂验证提升精度。
摘要：
相机标定是机器人和计算机视觉中的关键步骤。准确的相机参数对于实现稳健的应用是必要的。如今，相机标定过程包括将一组数据调整到针孔模型，假设重投影误差接近于零时，相机参数即为正确。由于所有相机参数都是未知的，计算结果被视为真实值。然而，如果考虑对焦因素，针孔模型并不能准确表示相机的行为。真实相机会轻微改变焦距以获得图像中清晰的物体，如果以恒定焦距计算唯一的针孔模型，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，以检测并加强其弱点。相机被安装在机器人手臂上，以准确获知外部相机参数，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，提出了一种对广泛接受的、使用平面模板图像的相机标定方法的改进。提出了一种具有距离相关焦距的针孔模型，以大幅改进标定过程。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
