Research on 6DOF Pose Measurement Algorithm of Industrial Metal Workpiece Based on Binocular Vision

In the face of the promotion of robot positioning and grasping applications in the industrial field, accurate identification and positioning of target objects in complex industrial environments have become a research highlights. To solve the problem of visual positioning interference caused by local reflections on industrial metal workpieces and obtain high-precision pose measurement results, this paper proposes a binocular vision 6DOF pose measurement strategy. Firstly, in view of the difficulties in manually collecting images and the high cost of collection, a data augmentation method based on generative adversarial networks is applied to achieve data augmentation and expand the the metal object image datasets. Then, in response to the problem of deformation and missing target objects in the images generated by the image data augmentation model, this paper proposes a method of changing the model architecture to improve the model's feature extraction ability for target objects and enrich the detailed features of the output images. Finally, aiming at the problem that the reflection of metal workpieces leads to incomplete information collection of the target object by the visual system and affects the pose measurement, this paper proposes a deep learning-based multi-keypoint detection method to provide effective data input for binocular vision 6DOF pose measurement. Combined with the binocular vision triangulation method to obtain the three-dimensional information of the target object, the pose measurement accuracy of the target object's 6DOF is improved. Experimental results show that this strategy outputs pose measurement data with high accuracy, providing data support for binocular vision-guided robot positioning and grasping.