Binocular Vision Object Positioning Method for Robots Based on Coarse-fine Stereo Matching

In order to improve the low positioning accuracy and execution efficiency of the robot binocular vision, a binocular vision positioning method based on coarse-fine stereo matching is proposed to achieve object positioning. The random fern is used in the coarse matching to identify objects in the left and right images, and the pixel coordinates of the object center points in the two images are calculated to complete the center matching. In the fine matching, the right center point is viewed as an estimated value to set the search range of the right image, in which the region matching is implemented to find the best matched point of the left center point. Then, the similar triangle principle of the binocular vision model is used to calculate the 3D coordinates of the center point, achieving fast and accurate object positioning. Finally, the proposed method is applied to the object scene images and the robotic arm grasping platform. The experimental results show that the average absolute positioning error and average relative positioning error of the proposed method are 8.22 mm and 1.96% respectively when the object's depth distance is within 600 mm, the time consumption is less than 1.029 s. The method can meet the needs of the robot grasping system, and has better accuracy and robustness.