基于2.5D标定扇的线阵相机内参数标定方法

Internal parameter calibration method of line-scan camera based on 2.5D calibration fan

针对工业生产线上或集成设备中线阵相机内参数定期检校困难且成本高的问题,提出一种基于2.5D标定扇的线阵相机内参数标定方法。设计了一款特定的2.5D标定扇,构建包含镜头畸变的线阵相机内参数标定模型,顾及了标靶相对相机的两个姿态角,通过方程线性变换法解算模型参数初值,利用改进的Levenberg-Marquardt(L-M)算法加速优化相机参数的求解过程,给出了详细的解算步骤和数据处理过程,并通过仿真和实测数据验证了方法的可行性。结果表明:该线阵相机标定方法简单灵活,可得到大量分布规律的特征点对,参数标定精度不受相机移动精度和特定移动方向的限制;在扇骨面与靶面间夹角小于10°时,可以获得高精度、高一致性的相机内参数,特征点重投影误差的最大值优于0.28 pixel,平均RMSE为0.11 pixel,标准差仅0.01 pixel。

Objective Aiming at the difficulty and high cost of regular calibration of line-scan camera internal parameters in industrial production lines or integrated equipment,a calibration method of line-scan camera internal parameters based on 2.5D calibration fan is proposed.The appropriate calibration object is designed,and the internal parameter calibration model of the linear array camera is established.The linear transformation theory is used to calculate the initial value of the model parameters,and the improved Levenberg-Marquardt(L-M)algorithm is used to optimize the camera parameters.The experimental results show that the internal parameters of the linear array camera calibrated by this method have high accuracy and good consistency.The maximum reprojection error is less than 0.28 pixel,and the average RMSE is 0.112 pixel.Methods A specific 2.5D calibration fan was designed.The internal parameter calibration model of line-scan camera including lens distortion is constructed,which takes into account the two attitude angles of the target relative to the camera.The initial value of the model parameters is solved by the equation linear transformation method,and the improved L-M algorithm is used to accelerate the optimization of the camera parameters.The detailed calculation steps and data processing process are given,and the feasibility of the method is verified according to the simulation and measured data.Results and Discussions The theoretical analysis and experimental results show that the linear array camera calibration method is simple and flexible,and a large number of feature point pairs with regular distribution can be obtained.The parameter calibration accuracy is not limited by the camera movement accuracy and specific direction.In addition,when the angle between the fan-bone surface and the target surface is less than 10°,highprecision and high-consistency camera internal parameters can be obtained.The maximum value of the feature point reprojection error is better than 0.28 pixel,the average RMSE is 0.112 pixel,and the standard deviation is only 0.014 pixel.Conclusions Line-scan cameras are often placed inside the equipment in a modular form with other sensors,and the regular calibration of the internal parameters of such cameras is costly and difficult.In view of this,an internal parameter calibration method of linear array camera based on 2.5D calibration fan is proposed.The 2.5D calibration fan has the advantages of both the three-dimensional measurement effect of the 3D target and the low cost and high precision of the 2D target.The number of feature points is large and the distribution is regular,which avoids the problem of easy loss of features,and the feature points and image points are easy to match.The constructed linear array camera calibration model takes into account the lens distortion error and the small angle attitude between the target surface and the image surface,so that the camera movement direction and the position of the calibration object are not strictly limited.Experiments show that the internal parameters of the camera calibrated by the fan bone with<±10°are the best,and the calibration results have high accuracy and good consistency.The reprojection error of 89%feature points is less than 0.20 pixel,the maximum error is better than 0.28 pixel,and the average RMSE is 0.112 pixel.In addition,compared with the standard L-M algorithm,the improved L-M algorithm reduces the number of iterations by half without affecting the accuracy of parameter optimization.