Automatic detection and sorting of corners by improved chessboard pattern

In automatic camera calibration with traditional black and white chessboard patterns, the corner sorting results are usually influenced by the rotation angle of the calibration pattern. Therefore, this paper designs an improved chessboard pattern and corresponding automatic corner detection and sorting algorithm. In the new pattern, four rectangular boundaries were added to filter the complex background, and a double-triangle mark was used to determine the original point of the corners so as to adapt to the rotation of the pattern. A corner detection algorithm based on cross entropy of the symmetrical quadrant was proposed to implement the corner position with the accuracy of pixel level by local non-maximum suppression and rectangular selection. Then, the Frostner operator was used to calculate the sub-pixel coordinates of the corners. According to the detected corner, the curve fitting method was employed to realize the automatic corner sorting with the distance information between the corners and the origin. Experiment results show that the corner detection results are correct and the sub-pixel coordinate error between the new method and Matlab Calibration Toolbox is less than 0. 8 pixel unit. Moreover, the sorting results show an invariance to the pattern rotation, which verifies that the method is suitable for online auto camera calibration.