Improve three-dimensional point localization accuracy in stereo vision systems using a novel camera calibration method

Abstract

Computer vision systems have demonstrated to be useful in applications of autonomous navigation, especially with the use of stereo vision systems for the three-dimensional mapping of the environment. This article presents a novel camera calibration method to improve the accuracy of stereo vision systems for three-dimensional point localization. The proposed camera calibration method uses the least square method to model the error caused by the image digitalization and the lens distortion. To obtain particular three-dimensional point coordinates, the stereo vision systems use the information of two images taken by two different cameras. Then, the system locates the two-dimensional pixel coordinates of the three-dimensional point in both images and coverts them into angles. With the obtained angles, the system finds the three-dimensional point coordinates through a triangulation process. The proposed camera calibration method is applied in the stereo vision systems, and a comparative analysis between the real and calibrated three-dimensional data points is performed to validate the improvements. Moreover, the developed method is compared with three classical calibration methods to analyze their advantages in terms of accuracy with respect to tested methods.