Increasing the accuracy for computer vision systems by removing the noise

Robots rely on the computer vision systems to obtain the environmental information. As a result, the accuracy of the computer vision systems is essential for the control of the robots. Many computer vision systems make use of markers of the well-designed patterns to calculate the system parameters. Undesirably, the noise exists universally, which decreases the calibration accuracy and consequently decreases the accuracy of the computer vision systems. In this paper, we propose a pattern modeling method to remove the noise by decreasing the degree of freedom of the total calibration markers to one. The theorem is proposed and proved. The proposed method can be readily adopted by different computer vision systems, e.g. structured light based computer vision systems and stereo vision based systems.