基于卡尔曼滤波的摄像机标定方法

Camera calibration method based on Kalman filter

本文提出了一种基于迭代扩展卡尔曼滤波的摄像机标定方法。将二维平面靶标图像上的特征点看作是匀速运动的点,以观测到的特征点图像坐标和对应世界坐标作为滤波器的输入,摄像机内外参数的估计值作为滤波器的输出,根据迭代扩展卡尔曼滤波算法得到摄像机内外参数的最优估计。通过仿真和真实实验,结果表明,对于有限数量的平面靶标标定图像数据,该算法具有较高的标定精度和较好的鲁棒性。

A camera calibration method based on the Iterated Extended Kalman Filter （IEKF） was proposed in this paper. The feature points in two-dimensional planar target images were considered in uniform motion. Taking the image coordinates and the corresponding world coordinates of the observed feature points as the filter inputs and taking the estimated value of the intrinsic and extrinsic camera parameters as the filter outputs, the optimized values of the intrinsic and extrinsic camera parameters were obtained with IEKF algorithm. Simulation and real experiments to evaluate the performance of the proposed method on test data are reported, and the results show that this method is robust and feasible algorithm with good precision for a few calibrated images of planar target.