Geometric Calibration for Cameras with Inconsistent Imaging Capabilities.

Traditional calibration methods rely on the accurate localization of the chessboard points in images and their maximum likelihood estimation (MLE)-based optimization models implicitly require all detected points to have an identical uncertainty. The uncertainties of the detected control points are mainly determined by camera pose, the slant of the chessboard and the inconsistent imaging capabilities of the camera. The negative influence of the uncertainties that are induced by the two former factors can be eliminated by adequate data sampling. However, the last factor leads to the detected control points from some sensor areas having larger uncertainties than those from other sensor areas. This causes the final calibrated parameters to overfit the control points that are located at the poorer sensor areas. In this paper, we present a method for measuring the uncertainties of the detected control points and incorporating these measured uncertainties into the optimization model of the geometric calibration. The new model suppresses the influence from the control points with large uncertainties while amplifying the contributions from points with small uncertainties for the final convergence. We demonstrate the usability of the proposed method by first using eight cameras to collect a calibration dataset and then comparing our method to other recent works and the calibration module in OpenCV using that dataset.