Abstract: An ORB infrared binocular ranging method based on adaptive geometric constraints and the random sampling consistency method is proposed to address the issues of low computational efficiency, high mismatching rates, and insufficient accuracy in binocular vision measurements by traditional feature matching algorithms. First, key points are detected and described using the FAST and BRIEF algorithms, and the initial matching of feature points is performed using the fast library for approximate nearest neighbors (FLANN) algorithm. Then, based on the slope and distance of the initial matching pairs, appropriate thresholds are selected, and geometric constraints based on these parameters are constructed to eliminate incorrect matching pairs. Finally, a random sample consensus (RANSAC) method is used to remove anomalous points and complete the fine matching. The distance of the target object is calculated by combining the thermal camera calibration parameters. Experimental results show that the improved ORB algorithm yields higher quality feature points and greater measurement accuracy compared to traditional algorithms, with an average absolute error of distance measurements at 1.64%, demonstrating its practical value.