论文标题
基于体积的接触点检测7-DOF抓握
Volumetric-based Contact Point Detection for 7-DoF Grasping
论文作者
论文摘要
在本文中，我们提出了一条基于截短的签名距离函数（TSDF）体积的接触点检测的新型掌握管道，以实现闭环7度自由度（7-DOF）在杂物环境上抓住。我们方法的关键方面是1）根据多视图融合，接触点采样和评估以及碰撞检查，提议的管道利用TSDF音量，该量提供可靠且无碰撞的7-DOF抓手姿势，并具有实时性能； 2）基于接触的姿势表示有效地消除了基于正常的方法引入的歧义，从而提供了更精确，更灵活的解决方案。广泛的模拟和实体机器人实验表明，在模拟和物理场景中，就掌握成功率而言，提出的管道可以选择更多的反物和稳定的抓握姿势，并优于基于正常的基线。
In this paper, we propose a novel grasp pipeline based on contact point detection on the truncated signed distance function (TSDF) volume to achieve closed-loop 7-degree-of-freedom (7-DoF) grasping on cluttered environments. The key aspects of our method are that 1) the proposed pipeline exploits the TSDF volume in terms of multi-view fusion, contact-point sampling and evaluation, and collision checking, which provides reliable and collision-free 7-DoF gripper poses with real-time performance; 2) the contact-based pose representation effectively eliminates the ambiguity introduced by the normal-based methods, which provides a more precise and flexible solution. Extensive simulated and real-robot experiments demonstrate that the proposed pipeline can select more antipodal and stable grasp poses and outperforms normal-based baselines in terms of the grasp success rate in both simulated and physical scenarios.
