Robust and accuracy calibration method for a binocular camera using a coding planar target

High-accuracy binocular camera calibration is a vital basis of precise binocular vision 3D measurement. In this work, a high-precision and robust binocular camera calibration method based on a coding target is proposed. First, a coding target with the simple patterns is designed. Every corner on the coding target has a unique code number, which can make the identification of homonymous corners easier and more valuable, even if the target is partially occluded. The decoding of the coding target is rapid, robust, and accurate at a complex background. Subsequently, the zenith and azimuth angles are introduced in the proposed calibration method to study the effect of the orientation of the coding target on the stability of calibration results and improve the robustness of the calibration results. Finally, to fully utilize the 3D information of the calibration corners on the coding target, we combine the reprojection and 3D geometric constraints to propose a multi-constraint optimization method for refining the parameters of binocular camera and improving the accuracy of binocular camera calibration. The comparison experiments have been done to verify the performance of the proposed calibration method. The standard deviations of the intrinsic and extrinsic parameters are greatly decreased, compared with Zhang's method. The mean reprojection and 3D geometric errors calculated by the proposed method have a large reduction. And the application experiment furtherly validates the effectiveness of the proposed method.

Camera calibration using a planar target with pure translation

This paper presents a novel camera calibration method using a planar target with pure translation and a known translation distance. It only requires the straightness and position accuracy of the one-axis translational platform. There are theoretically no constraints among the normal direction of the planar target, the optical axis direction of the camera, and the moving direction of the translational platform. The paper analyzes the closed-form solution, followed by a nonlinear refinement based on the maximum likelihood criterion. Both computer simulation and real data are implemented to verify the effectiveness of the proposed method. Compared with Zhang's method, the proposed method realizes the camera calibration process automatically and evaluates the calibration process via the measurement accuracy of the calibrated camera, so it is a key factor to advance 3D computer vision one more step from expert to novice use.

3D Vision by Using Calibration Pattern with Inertial Sensor and RBF Neural Networks

Camera calibration is a crucial prerequisite for the retrieval of metric information from images. The problem of camera calibration is the computation of camera intrinsic parameters (i.e., coefficients of geometric distortions, principle distance and principle point) and extrinsic parameters (i.e., 3D spatial orientations: ω, ϕ, κ, and 3D spatial translations: t_x, t_y, t_z). The intrinsic camera calibration (i.e., interior orientation) models the imaging system of camera optics, while the extrinsic camera calibration (i.e., exterior orientation) indicates the translation and the orientation of the camera with respect to the global coordinate system. Traditional camera calibration techniques require a predefined mathematical-camera model and they use prior knowledge of many parameters. Definition of a realistic camera model is quite difficult and computation of camera calibration parameters are error-prone. In this paper, a novel implicit camera calibration method based on Radial Basis Functions Neural Networks is proposed. The proposed method requires neither an exactly defined camera model nor any prior knowledge about the imaging-setup or classical camera calibration parameters. The proposed method uses a calibration grid-pattern rotated around a static-fixed axis. The rotations of the calibration grid-pattern have been acquired by using an Xsens MTi-9 inertial sensor and in order to evaluate the success of the proposed method, 3D reconstruction performance of the proposed method has been compared with the performance of a traditional camera calibration method, Modified Direct Linear Transformation (MDLT). Extensive simulation results show that the proposed method achieves a better performance than MDLT aspect of 3D reconstruction.