轻量级6自由度抓取检测模型，可在边缘设备上实时运行。
Efficient End-to-End 6-Dof Grasp Detection Framework for Edge Devices with Hierarchical Heatmaps and Feature Propagation
- 采用分层热图与特征传播，端到端实现高效抓取检测
- 在边缘设备上实现实时推理，真实场景抓取成功率达94%
- 适合移动机器人在资源受限环境下部署使用
6-DoF抓取检测对智能体系统的发展至关重要，可为物体抓取提供可行的机器人位姿。现有方法多通过提取RGBD或点云数据的3D几何特征来检测6-DoF抓取，但多数因计算开销大，在真实机器人部署中面临挑战，尤其在依赖边缘计算的移动平台。本文提出一种高效端到端抓取检测网络E3GNet，利用分层热图表示进行6-DoF抓取检测。E3GNet能有效识别复杂真实环境中的高质量、多样化抓取。得益于端到端设计与高效网络结构，该方法在模型推理效率上优于先前方法，可在边缘设备上实现实时6-DoF抓取检测。真实实验验证了其有效性，实现了94%的物体抓取成功率。
原文摘要 · Abstract (English)
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.
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