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In order to tackle the challenges related to poor positioning accuracy and high usage cost of 6DOF visual measurement systems in industrial settings, this paper presents a monocular vision-based robot vision guidance approach. The goal is to address the issues of expensive 6DOF pose measurement and limited measurement robustness when robots need to manipulate metal objects in industrial environments. The proposed approach enables precise and robust measurement of the 6DOF pose of the target workpiece. The approach integrates two main algorithms: a virtual reality-based image data enhancement algorithm and a 6DOF pose measurement algorithm that combines a multi-keypoint detection model and the Efficient Perspective-n-Points (EPnP) algorithm. The image data enhancement algorithm enhances the data of small-sample industrial objects using image enhancement techniques. This improves the robustness of the detection model by mitigating the challenges of high-cost image acquisition and long acquisition time associated with industrial objects. On the other hand, the 6DOF pose measurement algorithm performs the pose measurement of the target workpiece using a single image, enabling cost-effective 6DOF pose measurement by utilizing only a monocular camera. Experimental results demonstrate that the proposed method achieves measurement errors of 4.21% in the X direction, 2.94% in the Y direction, and 0.39% in the Z direction of the target workpiece. These results highlight the effectiveness of the proposed approach in achieving accurate and reliable pose measurement.

Research on Robot Monocular Vision-based 6DOF Object Positioning and Grasping Approach Combined with Image Generation Technology