Optimization Design of Camera Pose for Network Base on Multi-view Stereo Vision

Abstract

When designing a multi-vision stereo vision network, the camera’s positional information has a critical impact on the measurement accuracy. In order to solve the problem of optimizing the camera pose during network design, in this paper, we split the multi-vision stereo vision system into binocular stereo vision system. Based on the mathematical model of binocular stereo vision system, the error function of the positional parameters is constructed. By simulation analyzing the function models the field of view angle, the angle between the optical axis and the baseline, the baseline length, the law of the influence of each parameter on the measurement accuracy and the optimal range of values are obtained. Then we compare the actual measurement results with the simulation analysis results. The results show that as the field of view angle, optical axis and baseline angle, baseline length increases, the measurement error first decreases and then increases, and the increase is gradually accelerated. When each parameter is located in the optimal range, the measurement error meets the precision engineering measurement accuracy requirement of 0.05mm, and the minimum error is reduced by 0.114mm, 0.120mm and 0.061mm compared to the maximum error. In the network design, the optimization of the pose parameters can effectively obtain better pose information of the camera and thus improve the accuracy of the measurement.