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Binocular vision calibration is of great importance in 3D machine vision measurement. With respect to binocular vision calibration, the nonlinear optimization technique is a crucial step to improve the accuracy. The existing optimization methods mostly aim at minimizing the sum of reprojection errors for two cameras based on respective 2D image pixels coordinate. However, the subsequent measurement process is conducted in 3D coordinate system which is not consistent with the optimization coordinate system. Moreover, the error criterion with respect to optimization and measurement is different. The equal pixel distance error in 2D image plane leads to diverse 3D metric distance error at different position before the camera. To address these issues, we propose a precise calibration method for binocular vision system which is devoted to minimizing the metric distance error between the reconstructed point through optimal triangulation and the ground truth in 3D measurement coordinate system. In addition, the inherent epipolar constraint and constant distance constraint are combined to enhance the optimization process. To evaluate the performance of the proposed method, both simulative and real experiments have been carried out and the results show that the proposed method is reliable and efficient to improve measurement accuracy compared with conventional method.

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