Accurate features point extraction for omnidirectional camera calibration

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.