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Due to the traditional Zhang Zhengyou’s camera calibration method of picking robot existed the problems such as sensitive to initial value of camera model parameters and instability of calibration results, a camera calibration method based on improved shuffled frog leaping optimization and LM algorithm was proposed. The camera calibration was divided into two steps: the first step, calculating the initial values of the parameters of camera model with the shuffled frog leaping optimization, which avoided the sensitivity to the initial value of the camera model parameters that was directly calculated with the traditional Zhang Zhengyou’s camera calibration method; the second step, refining the initial values of the parameters of camera model that calculated in the first step with improved nonlinear optimization LM algorithm, which avoided must obtaining the Jacobi matrix to optimize the parameters of the camera model with the Zhang Zhengyou’s camera calibration method, which led to the instability of the calibration results. And the binocular vision calibration system of the picking robot was developed by OpenCV. The camera calibration experiments were carried out on the traditional Zhang Zhengyou’s camera calibration method, the camera calibration method based on genetic algorithm, the camera calibration method based on shuffled frog leaping optimization algorithm and the camera calibration method. The test results showed that the absolute error of the left camera focal length was 0.065~0.100mm, the relative error of the left camera focal length was 1.899%~12.652%, the average pixel error of the left plane target image was 0.166~0.175 pixel, the absolute error of the right camera focal length was 0.083~0.360mm, the relative error of the right camera focal length was 2.429%~11.484%, the average pixel error of the right plane target image was 0.103~0.114 pixel and the absolute error of distance of binocular camera was 1.866~2.789mm, the relative error of the distance between the binocular camera was 3.209%~4.874%, the convergence speed and stability, which were obtained by the camera calibration method, were all better than the other camera calibration methods in the above. So, these test results verified the calibration parameters obtained by the method had high accuracy and reliability.