6-DoF grasp detection is critically important for the advancement of intelligent embodied systems, as it provides feasible robot poses for object grasping. Various methods have been proposed to detect 6-DoF grasps through the extraction of 3D geometric features from RGBD or point cloud data. However, most of these approaches encounter challenges during real robot deployment due to their significant computational demands, which can be particularly problematic for mobile robot platforms, especially those reliant on edge computing devices. This paper presents an Efficient End-to-End Grasp Detection Network (E3GNet) for 6-DoF grasp detection utilizing hierarchical heatmap representations. E3GNet effectively identifies high-quality and diverse grasps in cluttered real-world environments. Benefiting from our end-to-end methodology and efficient network design, our approach surpasses previous methods in model inference efficiency and achieves real-time 6-Dof grasp detection on edge devices. Furthermore, real-world experiments validate the effectiveness of our method, achieving a satisfactory 94% object grasping success rate.

翻译：六自由度抓取检测对于智能具身系统的进展至关重要，其为物体抓取提供了可行的机器人位姿。现有方法多通过从RGBD或点云数据中提取三维几何特征来检测六自由度抓取。然而，这些方法大多在真实机器人部署中面临挑战，主要源于其巨大的计算需求，这对于移动机器人平台——尤其是依赖边缘计算设备的平台——可能构成显著问题。本文提出了一种利用分层热图表征的高效端到端抓取检测网络（E3GNet），用于六自由度抓取检测。E3GNet能够在杂乱的真实世界环境中有效识别高质量且多样化的抓取位姿。得益于我们的端到端方法与高效网络设计，本方法在模型推理效率上超越了先前方法，并在边缘设备上实现了实时的六自由度抓取检测。此外，真实环境实验验证了本方法的有效性，取得了令人满意的94%物体抓取成功率。