The distorted checkerboard image affects the precision calibration in omnidirectional camera calibration due to inaccurate localization of features points. To solve this problem, an iterative refinement method is presented. Firstly, the initial-parameters are obtained from the traditional calibration method and the distorted checkerboard images are corrected to world coordinate system. Then, the features points are located in those undistorted images. The calibration parameters are recomputed in an iterative refinement until convergence. This iterative refinement method improves localization accuracy of feature points and consequently of camera calibration. The correctness and effectiveness of the method is proved by simulation experiments and physical experiments. The experiments show that the rep rojection error is reduced by 38% compared to traditional methods.
Circular target has an important application in pose estimation based on vision by the virtue of anti-occlusion, anti-noise and easy recognition on the image. This paper focuses on the pose estimation problem when the projection of circle center is determined, and a pose calculation method is proposed based on 1D homography. Firstly, under the normalized image coordinates, a homography matrix is determined from three points, one is the projection of circle center, the other two are the intersections of the line through the projected center and the projected ellipse. According to the geometrical properties of the homography matrix, a linear method for computing the circular pose from multiple homography matrices is presented. Secondly, the reprojection errors of image points are taken as the objective function to optimize the linear solution results. Finally, the proposed method is compared with the two existing pose estimation methods through experiment. The experimental results show that the proposed method is slightly superior to the existing methods in terms of anti-noise performance and has obvious advantages in remote pose estimation.