Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong SAR, China.
Sensors (Basel). 2022 Apr 2;22(7):2739. doi: 10.3390/s22072739.
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.
传统的标定方法依赖于棋盘格点在图像中的精确定位，其最大似然估计（MLE）优化模型隐含地要求所有检测到的点具有相同的不确定性。检测到的控制点的不确定性主要由相机姿态、棋盘的倾斜度和相机不一致的成像能力决定。前两个因素引起的不确定性的负面影响可以通过充分的数据采样来消除。然而，最后一个因素导致来自某些传感器区域的检测到的控制点比来自其他传感器区域的控制点具有更大的不确定性。这导致最终标定的参数过度拟合位于较差传感器区域的控制点。在本文中，我们提出了一种测量检测到的控制点不确定性的方法，并将这些测量到的不确定性纳入几何标定的优化模型中。新模型抑制了具有较大不确定性的控制点的影响，同时放大了具有较小不确定性的点对最终收敛的贡献。我们通过使用八台相机收集标定数据集来演示所提出方法的可用性，然后使用该数据集将我们的方法与其他最近的工作和 OpenCV 中的标定模块进行比较。
