Automatic detection of checkerboards on blurred and distorted images

Most of the existing camera calibration toolboxes require the observation of a checkerboard shown by the user at different positions and orientations. This paper presents an algorithm for the automatic detection of checkerboards, described by the position and the arrangement of their corners, in blurred and heavily distorted images. The method can be applied to both perspective and omnidirectional cameras. An existing corner detection method is evaluated and its strengths and shortcomings in detecting corners on blurred and distorted test image sets are analyzed. Starting from the results of this analysis, several improvements are proposed, implemented, and tested. We show that the proposed algorithm is able to consistently identify 80% of the corners on omnidirectional images of as low as VGA resolution and approaches 100% correct corner extraction at higher resolutions, outperforming the existing implementation significantly. The performance of the proposed method is demonstrated on several test image sets of various resolution, distortion, and blur, which are exemplary for different kinds of camera-mirror setups in use.