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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.

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In current science and engineering, the demand for large-size surface detection has increased considerably. However, large-size surface detection presents some challenges, such as very large detection target area, discontinuous detection surface, and low detection accuracy. The current detection methods are mainly based on in-position detection and cannot meet the requirements of detection speed and accuracy for large-size surface detection. In this paper, we propose a fast detection method for large-scale flatness based on an intelligent vision mobile platform (IVMP). Specifically, by establishing the path optimization model, beam adjustment model, and largescale flatness calculation model for the IVMP, the binocular vision acquisition of large-scale target information and fast large-scale shape detection are realized. The rigidly fixed position relation of binocular vision is considered, and the parameters of the main camera can be obtained through an error equation, then the parameters of the assistant camera can be acquired quickly to calculate the three-dimensional coordinates of space points. The particle swarm optimization algorithm is integrated into the differential evolution algorithm to improve the detection speed. The IVMP is applied to the flatness detection of a satellite antenna. The experimental results show that the detection precision and efficiency of the IVMP are clearly higher than those of the laser tracking and theodolite systems.

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