In camera calibration,accurate estimation of homography matrix between the world coordinates of the calibration board and its image coordinates is a key step in high-precision calibration of intrinsic camera parameter...In camera calibration,accurate estimation of homography matrix between the world coordinates of the calibration board and its image coordinates is a key step in high-precision calibration of intrinsic camera parameters.The existing homography matrix estimation methods have problems such as dependence on thresholds,low computational efficiency,and initial model or sorting quality affecting results.In this paper,a homography matrix estimation method based on adaptive genetic algorithm was proposed.Firstly,a new circular grid calibration board was designed and the strategy of first sampling of data sets was optimized.Secondly,a mathematical model for the estimated homography matrix was established according to the adaptive genetic algorithm.Thereby the optimal homography matrix between the calibration board and its image was obtained.Finally,the intrinsic camera parameters were calculated based on Zhang’s calibration method.The experimental results show that compared with the results of three traditional estimation methods RANSAC,PROSAC,and LMEDS,the reprojection error of the images by our estimation method is reduced by about 4.11%-7.85%,11.94%-16.91%,and 10.19%-17.82%,respectively;and the average running time of the algorithm decreases by about 25.85%-37.47%,11.99%-22.71%,and 46.50%-53.35%,respectively.In addition,the homography matrix estimation method in this paper was applied to camera calibration.The results show that compared with the traditional estimation method,the average accuracy of the camera during the calibration process increases by about 5.48%,15.06%,and 11.47%,respectively;and the average calibration efficiency of the camera is improved by about 10.13%,5.71%,and 14.26%,respectively.The homography matrix estimation method proposed in this paper not only obtained reliable results,but also had certain value and significance in improving the estimation accuracy and calculation efficiency in camera calibration.展开更多
基金supported by Anhui Province Key Research and Development Program(No.2022107020012).
文摘In camera calibration,accurate estimation of homography matrix between the world coordinates of the calibration board and its image coordinates is a key step in high-precision calibration of intrinsic camera parameters.The existing homography matrix estimation methods have problems such as dependence on thresholds,low computational efficiency,and initial model or sorting quality affecting results.In this paper,a homography matrix estimation method based on adaptive genetic algorithm was proposed.Firstly,a new circular grid calibration board was designed and the strategy of first sampling of data sets was optimized.Secondly,a mathematical model for the estimated homography matrix was established according to the adaptive genetic algorithm.Thereby the optimal homography matrix between the calibration board and its image was obtained.Finally,the intrinsic camera parameters were calculated based on Zhang’s calibration method.The experimental results show that compared with the results of three traditional estimation methods RANSAC,PROSAC,and LMEDS,the reprojection error of the images by our estimation method is reduced by about 4.11%-7.85%,11.94%-16.91%,and 10.19%-17.82%,respectively;and the average running time of the algorithm decreases by about 25.85%-37.47%,11.99%-22.71%,and 46.50%-53.35%,respectively.In addition,the homography matrix estimation method in this paper was applied to camera calibration.The results show that compared with the traditional estimation method,the average accuracy of the camera during the calibration process increases by about 5.48%,15.06%,and 11.47%,respectively;and the average calibration efficiency of the camera is improved by about 10.13%,5.71%,and 14.26%,respectively.The homography matrix estimation method proposed in this paper not only obtained reliable results,but also had certain value and significance in improving the estimation accuracy and calculation efficiency in camera calibration.
