  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.
