LiDAR-assisted accuracy improvement strategy for binocular visual measurement

Abstract

The measurement model of binocular vision is inaccurate when the measurement distance is much different from the calibration distance, which affects its practicality. To tackle this challenge, we proposed what we believe to be a novel LiDAR-assisted accuracy improvement strategy for binocular visual measurement. First, the 3D points cloud and 2D images were aligned by the Perspective-n-Point (PNP) algorithm to realize calibration between LiDAR and binocular camera. Then, we established a nonlinear optimization function and proposed a depth-optimization strategy to lessen the error of binocular depth. Finally, the size measurement model of binocular vision based on the optimized depth is built to verify the effectiveness of our strategy. The experimental results show that our strategy can improve the depth accuracy compared to three stereo matching methods. The mean error of binocular visual measurement decreased from 33.46% to 1.70% at different distances. This paper provides an effective strategy for improving the measurement accuracy of binocular vision at different distances.