A Generative Feature-to-Image Robotic Vision Framework for 6D Pose Measurement of Metal Parts

Six degrees of freedom (6D) pose estimation or measurement of target objects are important robotic vision techniques that are key aspects for achieving autonomously executed robot tasks. The existing techniques solve this problem well for ordinary textured or rough-surfaced objects; however, 6D pose estimation remains a challenge for textureless, shiny metal parts in industry. This article proposes both a new framework for performing 6D pose estimation and a practical method based on the proposed framework that outperforms the state-of-the-art methods. All the existing methods follow an image-to-feature framework: they first extract features from images and then use the features to infer object poses. In contrast, our idea is a novel generative feature-to-image framework based on generative models, whose pipeline is a reverse mapping from feature to image. In other words, given a feature representing a pose, our method generates an image of the object in the exact same pose. Based on this framework, we propose a specific algorithm to indirectly infer the pose, which enables an initially random answer to gradually be regressed to the correct result. The experimental results demonstrate that the proposed deep learning method possesses greater robustness and achieves higher accuracy than the traditional methods