基于自适应EKF的摄像机标定优化方法  

Adaptive EKF-Based Camera Calibration Optimization Method

作　　者：赖欣 杨肖[2] 张启灿 Lai Xin;Yang Xiao;Zhang Qican(College of Electronics and Information Engineering,Sichuan University,Chengdu 610065,Sichuan,China;School of Mechanical Engineering,Southwest Petroleum University,Chengdu 610500,Sichuan,China)

机构地区：[1]四川大学电子信息学院,四川成都610065 [2]西南石油大学机电工程学院,四川成都610500

出　　处：《光学学报》2023年第23期191-200,共10页Acta Optica Sinica

基　　金：国家自然科学基金(62075143)。

摘　　要：针对扩展卡尔曼滤波算法在摄像机标定优化应用中,滤波精度较大程度地依赖于噪声协方差矩阵的准确性这一问题,提出了一种基于自适应扩展卡尔曼滤波算法的摄像机标定优化方法。以所检测到的二维棋盘格标靶上特征点的图像坐标作为自适应扩展卡尔曼滤波算法的观测量,摄像机的内、外参数作为状态量,将观测图像上的特征点进行逐点滤波运算,过程和观测噪声协方差矩阵在迭代过程中随着观测值和预测值之间新息的变化而更新,从而优化对应的摄像机参数。实验结果表明,经本文算法优化后获得的摄像机内、外参数具有较小的重投影误差,USB相机和工业相机的标定结果较张正友标定法分别提升了61.17%和12.17%,所提算法较无迹卡尔曼滤波算法和扩展卡尔曼滤波算法在噪声环境下具有更高的标定精度和更好的鲁棒性。Objective Camera calibration is significant in machine vision and is widely applied to 3D reconstruction,defect detection,visual navigation,etc.To improve the calibration result accuracy for intrinsic and extrinsic parameters,we propose a camera calibration optimization method based on the adaptive extended Kalman filter(AEKF)algorithm.Zhang's calibration method based on a 2D plane target is a commonly adopted camera calibration approach.Kalman filter(KF),extended Kalman filter(EKF),and unscented Kalman filter(UKF)have been introduced to further enhance the accuracy of Zhang's calibration method.The predicted value of the previous moment and observation value of the current moment are employed to accurately predict the state vector,providing an efficient and precise method to estimate the camera calibration state.EKF algorithm linearizes the nonlinear state equation by performing a first-order Taylor expansion of the nonlinear function and neglecting the other higher-order terms.Some scholars have applied the EKF algorithm to the camera calibration and yielded better calibration results than Zhang's calibration method.The introduction of a state estimation method can improve the camera calibration accuracy.However,the initial parameter setting of process and observation noises in the EKF algorithm,which affects the optimization of the camera calibration parameters,greatly depends on the user's judgment and choice,and has certain limitations and poor robustness in noisy environments.Therefore,we want to propose a method to perform the EKF-based camera calibration method without dependence on the initial parameter setting,update the process and observation noise covariance matrices employing the innovation between the predicted and observed values,and exhibit good robustness in noisy environments.Methods EKF cannot automatically select and adjust the process and observation noises in the camera calibration,which makes the camera calibration accuracy overly dependent on the user's judgment and inputs of the initial

关 键 词：机器视觉 摄像机标定 扩展卡尔曼滤波 新息 自适应 重投影误差 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]