Chen Chen, Jiang Xin
Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen 518055, China.
Sensors (Basel). 2024 May 25;24(11):3408. doi: 10.3390/s24113408.
Pose estimation of metal parts plays a vital role in industrial grasping areas. It is challenging to obtain complete point clouds of metal parts because of their reflective properties. This study introduces an approach for recovering the 6D pose of CAD-known metal parts from images captured by a single RGB camera. The proposed strategy only requires RGB images without depth information. The core idea of the proposed method is to use multiple views to estimate the metal parts' pose. First, the pose of metal parts is estimated in the first view. Second, ray casting is employed to simulate additional views with the corresponding status of the metal parts, enabling the calculation of the camera's next best viewpoint. The camera, mounted on a robotic arm, is then moved to this calculated position. Third, this study integrates the known camera transformations with the poses estimated from different viewpoints to refine the final scene. The results of this work demonstrate that the proposed method effectively estimates the pose of shiny metal parts.
金属部件的位姿估计在工业抓取领域起着至关重要的作用。由于金属部件的反射特性,获取其完整的点云具有挑战性。本研究介绍了一种从单台RGB相机拍摄的图像中恢复已知CAD模型的金属部件6D位姿的方法。所提出的策略仅需要无深度信息的RGB图像。该方法的核心思想是使用多个视图来估计金属部件的位姿。首先,在第一个视图中估计金属部件的位姿。其次,采用光线投射来模拟具有金属部件相应状态的其他视图,从而计算相机的下一个最佳视点。然后,将安装在机器人手臂上的相机移动到计算出的位置。第三,本研究将已知的相机变换与从不同视点估计的位姿相结合,以优化最终场景。这项工作的结果表明,所提出的方法有效地估计了有光泽金属部件的位姿。