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.

Aiming at the defects of traditional four-wheel aligner such as many sensors, complex operation and slow detection speed, a fast and accurate 3D four-wheel alignment detection method is studied. Firstly, a new and special circle center target board is designed to calibrate the camera, and then the registration of the homography matrix is optimized by using the improved RANSAC (Random sample consensus) algorithm combined with the designed special target board, and the parameters of the wheel alignment system are adjusted by using the space vector principle. Accurate measurements are made to obtain the parameters of the four-wheel alignment. Design a calibration comparison experiment between the traditional target board and the new type of target board, and conduct a comparative test with the existing four-wheel aligner of the depot. The experimental results show that the use of the new target board-binding optimization algorithm can improve the calibration efficiency by about 9% to 21%, while improving the calibration accuracy by about 10.6% to 17.8%. And through the real vehicle test, it is verified that the use of the new target combined with the optimization algorithm can ensure the accuracy and reliability of the four-wheel positioning. This method has a certain significance in the rapid detection of vehicle four-wheel alignment parameters.

A fast and accurate homography matrix method for four-wheel positioning detection was presented in the paper. Fewer sensors were required with simpler operation and faster detection. Firstly, eight feature points were extracted by using the target detection algorithm based on the fitting method. Secondly, six feature points were obtained by line fitting-based selection. Thirdly, from the selected six feature points, five points were randomly chosen to minimize the re-projection error. Finally, four points were randomly selected from these five feature points to find the homography matrix, and the other point was back to the homography matrix for verification. The experimental results show that the mean re-projection error is reduced by about 3.41%−4.57% compared with the modified RANSAC (Random sample consensus) algorithm. With the optimized algorithm, the error is reduced by about 12.81%−13.86% compared with the improved RANSAC algorithm. Compared with traditional targets, the average calibration time is reduced by about 26.95%−27.88%. The results indicated that the combination of target algorithm and optimization algorithm could ensure the accuracy and reliability of four-wheel positioning.