Institute of Robotics and Intelligent Systems, School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430081, China.
Sensors (Basel). 2022 Feb 24;22(5):1805. doi: 10.3390/s22051805.
Pose estimation is a particularly important link in the task of robotic bin-picking. Its purpose is to obtain the 6D pose (3D position and 3D posture) of the target object. In real bin-picking scenarios, noise, overlap, and occlusion affect accuracy of pose estimation and lead to failure in robot grasping. In this paper, a new point-pair feature (PPF) descriptor is proposed, in which curvature information of point-pairs is introduced to strengthen feature description, and improves the point cloud matching rate. The proposed method also introduces an effective point cloud preprocessing, which extracts candidate targets in complex scenarios, and, thus, improves the overall computational efficiency. By combining with the curvature distribution, a weighted voting scheme is presented to further improve the accuracy of pose estimation. The experimental results performed on public data set and real scenarios show that the accuracy of the proposed method is much higher than that of the existing PPF method, and it is more efficient than the PPF method. The proposed method can be used for robotic bin-picking in real industrial scenarios.
姿态估计是机器人分拣任务中的一个特别重要的环节。它的目的是获取目标物体的 6D 姿态（3D 位置和 3D 姿态）。在实际的分拣场景中，噪声、重叠和遮挡会影响姿态估计的准确性，导致机器人抓取失败。本文提出了一种新的点对特征（PPF）描述符，其中引入了点对的曲率信息，以增强特征描述，提高点云匹配率。所提出的方法还引入了一种有效的点云预处理方法，该方法可以在复杂场景中提取候选目标，从而提高整体计算效率。通过结合曲率分布，提出了一种加权投票方案，进一步提高了姿态估计的准确性。在公共数据集和真实场景上的实验结果表明，所提出的方法的准确性明显高于现有的 PPF 方法，并且比 PPF 方法更高效。所提出的方法可用于实际工业场景中的机器人分拣。
