Corner localization and camera calibration from imaged lattices

Abstract

This thesis proposes a model-based approach to improve the detection of calibration board fiducials from calibration imagery taken by wide-angle or fisheye lenses. From a single image, we estimate the camera model and project the calibration board into the image to guide the search for missed detections and reject spurious detections. In addition, we propose a classifier to label ambiguous detections that are geometrically plausible given the estimated camera model and imaged board. The proposed method addresses shortcomings of the state-of-the-art, which struggle to reliably detect board fiducials at the extents of the image, where the lens distortion is most observable. The proposed method recovers additional corners that can be used to place additional constraints on the non-convex camera calibration problem, which improves the likelihood of convergence to a global minimum. The code for this paper is available on GitHub.