论文标题
regnet：基于区域的抓地力网络，用于端到端的端云检测
REGNet: REgion-based Grasp Network for End-to-end Grasp Detection in Point Clouds
论文作者
论文摘要
在非结构化环境中可靠的机器人抓握是一项至关重要但具有挑战性的任务。主要问题是从部分噪声观察中产生对新物体的最佳掌握。本文提出了一个端到端的GRASP检测网络，将一个单视点云作为解决问题的输入。我们的网络包括三个阶段：得分网络（SN），GRASP区域网络（GRN）和精炼网络（RN）。具体而言，SN会回归点掌握置信度，并以高信心选择正点。然后，GRN对选定的正点进行掌握建议预测。 RN通过完善GRN预测的建议来产生更准确的掌握。为了进一步提高性能，我们提出了一种掌握锚固机制，其中引入了带有分配的抓地力方向的握把以产生抓紧建议。实验表明，在现实世界中，Regnet的成功率为79.34％，完成率为96％，这在包括GPD，PointNetNetGPD和S4G在内的几种基于点云的方法明显优于几种最先进的方法。该代码可在https://github.com/zhaobinglei/regnet_for_3d_grasping获得。
Reliable robotic grasping in unstructured environments is a crucial but challenging task. The main problem is to generate the optimal grasp of novel objects from partial noisy observations. This paper presents an end-to-end grasp detection network taking one single-view point cloud as input to tackle the problem. Our network includes three stages: Score Network (SN), Grasp Region Network (GRN), and Refine Network (RN). Specifically, SN regresses point grasp confidence and selects positive points with high confidence. Then GRN conducts grasp proposal prediction on the selected positive points. RN generates more accurate grasps by refining proposals predicted by GRN. To further improve the performance, we propose a grasp anchor mechanism, in which grasp anchors with assigned gripper orientations are introduced to generate grasp proposals. Experiments demonstrate that REGNet achieves a success rate of 79.34% and a completion rate of 96% in real-world clutter, which significantly outperforms several state-of-the-art point-cloud based methods, including GPD, PointNetGPD, and S4G. The code is available at https://github.com/zhaobinglei/REGNet_for_3D_Grasping.
