Abstract: In the process of applying robots to car body spot welding quality inspection, the positioning accuracy of its welding spots was affected by factors such as quality of spot welding operations and car body manufacturing errors, which resulted in actual welding spots did not coincide with designed values. Aiming at the problem that traditional teaching could not perform real-time compensation for welding spot positioning, a welding spots positioning strategy based on binocular vision guided robots was proposed, and a support vector machine regression error compensation model optimized based on improved particle swarm algorithm was constructed to compensate the positioning results. The binocular sensors were installed at the end of the robot, the binocular positioning principle was used to initially locate welding spots, and the measured data and actual data of welding spots position was used as learning samples. The trained error compensation model was used to predict positioning error of the system, and the compensation results were used as the correction value guiding the robot to locate welding spots. The experimental results show that the positioning accuracy after compensation is greatly improved, which verifies the effectiveness of the method.