Binocular Vision System Calibration Research Articles

A 3D calibration method for binocular vision system

Abstract Binocular vision system calibration is extremely important in three-dimensional (3D) measurement. During the calibration process, the establishment of an appropriate objective function has a direct impact on the calibration accuracy. For common two-dimensional (2D) calibration methods, differences in 2D calibration and 3D measurement coordinate systems result in loss of accuracy. Furthermore, for the newly proposed 3D calibration methods, the epipolar constraints in the 2D pixel coordinate system and the reconstruction errors in the 3D physical space need to be balanced by weights, resulting in uncertainties in the calibration results. To address these issues, we propose a novel calibration method that minimizes the distances between the control point and the left and right camera rays. The distances between the control points and the camera rays describe the 3D reconstruction errors, and the distances between the left camera and the right camera rays describe the geometry information between the two cameras to ensure that the epipolar constraints are satisfied. As a result, these two types of constraints/errors are measured under the same 3D coordinate system. Therefore, the epipolar constraints and the reconstruction errors are naturally balanced. We have conducted computer simulations and real experiments, and the results show that the proposed calibration method is effective in improving the accuracy compared with the state-of-the-art methods.

Precise Calibration of Binocular Vision System Based on Oblique Cone Projection Model

Precise Calibration of Binocular Vision System Based on Oblique Cone Projection Model

In Situ Calibration of Binocular Vision System for 3-D Measurement of Particle Size During Crystallization

In Situ Calibration of Binocular Vision System for 3-D Measurement of Particle Size During Crystallization

Industrial Binocular Vision System Calibration With Unknown Imaging Model

Industrial Binocular Vision System Calibration With Unknown Imaging Model

Binocular vision calibration and 3D re-construction with an orthogonal learning neural network

A new approach for binocular vision system calibration and 3D re-construction is proposed. While the system is calibrated, the sum of square distances between the vector coordinates recombined with...

Precise calibration of binocular vision system used for vision measurement.

Binocular vision calibration is of great importance in 3D machine vision measurement. With respect to binocular vision calibration, the nonlinear optimization technique is a crucial step to improve the accuracy. The existing optimization methods mostly aim at minimizing the sum of reprojection errors for two cameras based on respective 2D image pixels coordinate. However, the subsequent measurement process is conducted in 3D coordinate system which is not consistent with the optimization coordinate system. Moreover, the error criterion with respect to optimization and measurement is different. The equal pixel distance error in 2D image plane leads to diverse 3D metric distance error at different position before the camera. To address these issues, we propose a precise calibration method for binocular vision system which is devoted to minimizing the metric distance error between the reconstructed point through optimal triangulation and the ground truth in 3D measurement coordinate system. In addition, the inherent epipolar constraint and constant distance constraint are combined to enhance the optimization process. To evaluate the performance of the proposed method, both simulative and real experiments have been carried out and the results show that the proposed method is reliable and efficient to improve measurement accuracy compared with conventional method.

High Precision Calibration of Binocular Vision System Based on Genetic Algorithm

This paper presents a high precision calibration method based on genetic algorithm (GA) for binocular vision system, involving the problems of the GA optimization and the camera modeling. For the GA optimization, both the binary-code and the real-code are considered. The binary-code makes the feasible solution approach to the optimal solution rapidly, and then the accuracy of GA is further improved by using the real-code. Furthermore, in order to improve the convergence rate of the algorithm, the parameters of the two cameras are calculated separately, and the target function is selected by the forward mapping relationship between the world coordinates and the image coordinates. The experimental results demonstrate the effectiveness of the proposed approach.

Binocular vision system calibration based on a one-dimensional target

This paper proposes an adjustment method for binocular vision measurement to calibrate a camera's internal and external parameters based on a one dimensional (1D) target in the field of view. A 1D target with two feature points lying randomly in the field of view is used to get the images of the feature points. The distance between the two feature points is known. The internal and external parameters can be acquired by solving equations combining the photograph measurement collinear equations and the feature points' distance equations. To solve these equations, we use linearization of nonlinear equations and the adjustment method. During the process, we deal with the equations as measurement equations and the internal/external parameters and the 3D target points as the unknown parameters to calculate them. According to field experiment results, in about a 600 mm×600 mm field of view, the relative error of the distance of two points is less than two ten-thousandths, obtained by using the calculated results of the binocular vision system. The calibration process is simple, convenient, and suitable for calibrating a camera on the spot.

激光再制造机器人双目视觉系统标定研究

激光再制造机器人双目视觉系统标定研究