Accurate Camera Calibration with New Minimizing Function

Abstract

Camera calibration has been studied extensively in computer vision and photogrammetry. But almost all the camera calibration techniques iterate with the general minimizing function by minimizing the discrepancy between the real position in pixels of a 2D image point and the calculated projection of the 3D object point on the image plane. Though the imaging distance errors are equal, the spatial anti-projection distance errors are not identical at different distance before the camera. As far as vision measurement system, its final object is to obtain the accurate space coordinate of the measured point. Theoretically, the space point should on the optical ray generated by its projection image point and the center of camera. To satisfy the special request of vision measurement system for camera calibration parameters, we present a valid camera calibration method based on new minimizing function using high precision virtual stereo calibration pattern, which is formed by moving an infrared light-emitting diode (IR LED) feature point with CMM on pre-defined paths. Radial distortion and decentering distortion are molded. The proposed technique consists of linear optimization parameter estimation and nonlinear refinement, which is carried out by minimizing the distance of all the 3D space points from the corresponding optical ray generated by their projections image points and the center of camera. Simulated data and real data are both shown that the calibration precision of the proposed method is better than that of the general minimizing the distance between the imaged points and the modeled projections. This method considerable reduces the distance of all the 3D space points from the corresponding optical ray generated from their projections image points and the center of camera, enhances the precision of camera calibration parameters, and improves the precision of the vision measurement system.