Abstract
In the application of 3D reconstruction of multi-cameras, it is necessary to calibrate the camera used separately, and at the same time carry out multi-stereo calibration, and the calibration accuracy directly affects the effect of the 3D reconstruction of the system. Many researchers focus on the optimization of the calibration algorithm and the improvement of calibration accuracy after obtaining the calibration plate pattern coordinates, ignoring the impact of calibration on the accuracy of the calibration board pattern coordinate extraction. Therefore, this paper proposes a multi-camera stereo calibration method based on circular calibration plate focusing on the extraction of pattern features during the calibration process. This method preforms the acquisition of the subpixel edge acquisition based on Franklin matrix and circular feature extraction of the circular calibration plate pattern collected by the camera, and then combines the Zhang’s calibration method to calibrate the camera. Experimental results show that compared with the traditional calibration method, the method has better calibration effect and calibration accuracy, and the average reprojection error of the multi-camera is reduced by more than 0.006 pixels.
Highlights
Multi-camera calibration is a key step to achieve multi-vision detection and multi-view stereo vision
The latest monocular camera calibration algorithm based on improved particle swarm algorithm proposed by Tian et al effectively overcomes the problem of falling into local optimal solution [4]
All photos are divided into circular patterns, followed by the process shown in Algorithm 1 for circular subpixel edge extraction and center coordinate calculation, to obtain the coordinate matrix of the calibration pattern in each calibration plate, and use the calibration algorithm in the OpenCV library for camera calibration, and obtain the internal reference matrices of the left, middle and right cameras are Ml, Mm, Mr
Summary
Multi-camera calibration is a key step to achieve multi-vision detection and multi-view stereo vision. The following purposes can be achieved through multi-camera calibration, the first is to determine the conversion relationship between the three-dimensional points in the real scene and the pixels in the image, the second is to determine the distortion factor in the camera imaging process for image correction, and the third is to reduce the requirements on the acquisition equipment through calibration technology. Stereo calibration can calculate the relative posture between multiple cameras, thereby reducing the equipment requirements, which is conducive to the application of multi-eye vision inspection technology in real production. The one-to-one correspondence between the image points and the three-dimensional space points is established to calibrate the camera by extracting the corner points in the calibration object. This method is simple and efficient, is often used in the calibration process. The latest monocular camera calibration algorithm based on improved particle swarm algorithm proposed by Tian et al effectively overcomes the problem of falling into local optimal solution [4]
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