Binocular Stereo Vision Calibration Accuracy Evaluation Using Epipolar Constraint

The accuracy of binocular stereo vision calibration obtained using the mean of total residuals of left and right images is unsatisfactory. To overcome this limitation, this study proposes a method for evaluating the accuracy of binocular stereo vision calibration based on the epipolar constraint. The proposed method considers the constraint relationship between left and right image features in binocular stereo vision and global characteristics of camera calibration parameters. Based on the principle of minimum matching cost, a stereo feature matching method based on scale-invariant feature transform is used for corner detection and matching. The accuracy of binocular stereo vision calibration is evaluated based on the matching degree of measured corner points on the left and right image planes with their corresponding epipolar lines on the relative image plane. This proposed algorithm is added to the calibration algorithm to realize real-time evaluation of camera calibration accuracy during the calibration experiment. Experiments demonstrate that the proposed method is more accurate than the method using the mean of residuals, with accuracy increased by up to 54.0%.