An Accurate 1D Camera Calibration Based on Weighted Similar-Invariant Linear Algorithm

In recent years, researchers around the world have been researching and improving the technique of 1D calibration of cameras. The previous work has been primarily focused on reducing the motion constraints of one-dimensional calibration objects, however the accuracy of the existing methods still needs to be improved when random noise is introduces. In order to improve the accuracy of the one-dimensional calibration of the camera, in this paper, we propose a new calibration method by combining a weighted similar invariant linear algorithm and an improved nonlinear optimization algorithm. Specifically, we use the weighted similar invariant linear algorithm to obtain the camera parameters as the initial calibration parameters, and then optimize the parameters by using improved nonlinear algorithm. Finally, in the case of introducing random noise, the results of computer simulations and laboratory experiments show that when the noise level reaches 2 pixels, the parameter error of this method is mostly reduced to 0.2% compared with other methods, which verifies the feasibility of our proposed method.