Yuan Shuangjie, Ge Zhenpeng, Yang Lu
Fundamental Research Center, School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China.
Front Neurorobot. 2023 Jun 13;17:1136882. doi: 10.3389/fnbot.2023.1136882. eCollection 2023.
Accurately estimating the 6DoF pose of objects during robot grasping is a common problem in robotics. However, the accuracy of the estimated pose can be compromised during or after grasping the object when the gripper collides with other parts or occludes the view. Many approaches to improving pose estimation involve using multi-view methods that capture RGB images from multiple cameras and fuse the data. While effective, these methods can be complex and costly to implement. In this paper, we present a Single-Camera Multi-View (SCMV) method that utilizes just one fixed monocular camera and the initiative motion of robotic manipulator to capture multi-view RGB image sequences. Our method achieves more accurate 6DoF pose estimation results. We further create a new T-LESS-GRASP-MV dataset specifically for validating the robustness of our approach. Experiments show that the proposed approach outperforms many other public algorithms by a large margin. Quantitative experiments on a real robot manipulator demonstrate the high pose estimation accuracy of our method. Finally, the robustness of the proposed approach is demonstrated by successfully completing an assembly task on a real robot platform, achieving an assembly success rate of 80%.
在机器人抓取过程中准确估计物体的六自由度姿态是机器人技术中的一个常见问题。然而，当夹爪与其他部件碰撞或遮挡视野时，在抓取物体的过程中或之后，估计姿态的准确性可能会受到影响。许多改进姿态估计的方法涉及使用多视图方法，即从多个相机捕获RGB图像并融合数据。虽然这些方法有效，但实施起来可能复杂且成本高昂。在本文中，我们提出了一种单相机多视图（SCMV）方法，该方法仅利用一个固定的单目相机和机器人操纵器的主动运动来捕获多视图RGB图像序列。我们的方法实现了更准确的六自由度姿态估计结果。我们进一步创建了一个专门用于验证我们方法鲁棒性的新T-LESS-GRASP-MV数据集。实验表明，所提出的方法在很大程度上优于许多其他公开算法。在真实机器人操纵器上进行的定量实验证明了我们方法的高姿态估计精度。最后，通过在真实机器人平台上成功完成一项装配任务，以80%的装配成功率证明了所提出方法的鲁棒性。
