In this paper, we propose a method to estimate the exact location of a camera in a cyber-physical system using the exact geographic coordinates of four feature points stored in QR codes(Quick response codes) and the pixel coordinates of four feature points analyzed from the QR code images taken by the camera. Firstly, the P4P(Perspective 4 Points) algorithm is designed to uniquely determine the initial pose estimation value of the QR coordinate system relative to the camera coordinate system by using the four feature points of the selected QR code. In the second step, the manifold gradient optimization algorithm is designed. The rotation matrix and displacement vector are taken as the initial values of iteration, and the iterative optimization is carried out to improve the positioning accuracy and obtain the rotation matrix and displacement vector with higher accuracy. The third step is to convert the pose of the QR coordinate system with respect to the camera coordinate system to the pose of the AGV(Automated Guided Vehicle) with respect to the world coordinate system. Finally, the performance of manifold gradient optimization algorithm and P4P analytical algorithm are simulated and compared under the same conditions.One can see that the performance of the manifold gradient optimization algorithm proposed in this paper is much better than that of the P4P analytic algorithm when the signal-to-noise ratio is small.With the increase of the signal-to-noise ratio,the performance of the P4P analytic algorithm approaches that of the manifold gradient optimization algorithm.when the noise is same,the performance of manifold gradient optimization algorithm is better when there are more feature points.

翻译：本文提出一种方法，利用QR码中存储的四个特征点的精确地理坐标，以及从相机拍摄的QR码图像中解析出的四个特征点的像素坐标，来估计信息物理系统中相机的精确位置。首先，设计了P4P（四点透视）算法，利用所选QR码的四个特征点唯一确定QR坐标系相对于相机坐标系的初始位姿估计值。第二步，设计了流形梯度优化算法，将旋转矩阵和位移向量作为迭代初始值进行迭代优化，以提高定位精度，获得更高精度的旋转矩阵和位移向量。第三步，将QR坐标系相对于相机坐标系的位姿转换为自动导引车（AGV）相对于世界坐标系的位姿。最后，在相同条件下对流形梯度优化算法和P4P解析算法的性能进行了仿真比较。可以看出，当信噪比较小时，本文提出的流形梯度优化算法的性能远优于P4P解析算法。随着信噪比的增大，P4P解析算法的性能逐渐接近流形梯度优化算法。在噪声相同的情况下，特征点越多，流形梯度优化算法的性能越好。