Research on the multimodal fusion calibration method of the binocular visual system in a large-scale FOV

Calibration is vital for the binocular visual system. The high-precision measurement volume of the traditional binocular visual system is very small; therefore, it is difficult to measure large objects accurately. Accurate calibration of the binocular visual system is also extremely difficult in a large-scale field-of-view (FOV). Several attempts have been made to calibrate the binocular visual system in a large-scale FOV without recognizing the importance of depth information. A new method based on multimodal constraints has been proposed to calibrate the binocular visual system in a large-scale FOV whose height-width-depth is 2*2*2 m(3). The method fuses multiple constraints, i.e. the distance error constraint, the reprojection error constraint, the epipolar error constraint, the spatial points error constraint, and the regularization constraint. An objective function that integrates the above constraints has been derived. The construction method of the global Jacobi matrix, the adaptive weight adjustment method, and the Levenberg-Marquardt iterative algorithm are used to minimize the objective function. Comparison experiments have been carried out to validate the performance of our proposed method by measuring a 1.4 m long calibration gauge. The results show that the root mean square (RMS) of the residual error of our method is 0.0929 mm, Zhang's traditional method is 0.523 mm, and Xiao's photogrammetric method is 0.348 mm. The RMS of our method is 82% less than that of Zhang's method. Noise experiments show that the RMS of the residual error of our method is 45% less than Zhang's method with 0.1 level noise inference, which verifies the stability and reliability of our method.