针对多摄像机一维标定算法精度低、抗噪性和稳定性差等问题, 提出了基于消失点之间互相约束的多摄像机标定方法.为了避免径向畸变对成像造成的影响, 利用欧式空间位置约束的几何特性, 进行畸变参数的求取.通过靶标特征点约束结合摄像机的射影不变性排除杂点的干扰, 再利用空间消失点之间夹角一致性, 以及靶标特征点所构成的直线和消失点的反向射线平行性来求解摄像机的参数.当一维靶标任意运动时, 存在着无法区分靶标特征点对应的成像点临界问题, 采取反推理论数学分析法可事先避免该问题的出现.通过构建多摄像机系统进行标定实验, 可以看出该方法具有较高的标定精度, 且随着噪声的增加, 标定结果具有一定的抗噪性和稳定性；由相对误差值可知, 该方法可应用于多摄像机系统.

As for the one-dimensional multi-camera calibration algorithm of low precision, anti-manic and poor stability, a multi-camera calibration algorithm was proposed based on the mutual constraint among vanishing points. In order to avoid the impact of radial distortion caused by the imaging, the geometric characteristics of position constrains in Euclidean space were used to calculate the distortion parameters. By combining the constraints of target feature points with projective invariance of camera to eliminate interference of miscellaneous points, then the consistency of angles in the space vanishing points, line which consisted of target feature points and the parallelism of the reverse-ray of the vanishing points were used to calculate the camera parameters. When one-dimensional targets move randomly, a critical problem is existed which is inability to distinguish the target feature points corresponding to the image points, this problem can be avoided by taking use of the inverse theory of mathematical analysis. By constructing the multi-camera system to calibrate the experiments, we can see that this method has a higher precision calibration, the calibration results have a certain degree of noise immunity and stability with the increasing of noise. The relative error value shows that this method can be applied in multiple camera systems.