High precision grasp pose detection is an essential but challenging task in robotic manipulation. Most of the current methods for grasp detection either highly rely on the geometric information of the objects or generate feasible grasp poses within restricted configurations. In this letter, a grasp pose detection framework is proposed that generates a rich set of 6-DoF grasp poses with high precision. Firstly, a novel feature fusion module with multi-radius cylinder sampling is designed to enhance local geometric representation. Secondly, an optimized grasp operation head is developed to further estimate grasp parameters. Finally, a grasp pose propagation algorithm is proposed, which effectively extends grasp poses from a restricted configuration to a larger configuration. Experiments on a large-scale benchmark, GraspNet-1Billion, show that the proposed method outperforms existing methods (+8.61 AP). The real-world experiments further demonstrate the effectiveness of the proposed method in cluttered environments.

中文翻译：

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基于网络优化和姿态传播的杂乱场景中的高精度 6-DoF 抓取检测

高精度抓取姿势检测是机器人操作中一项重要但具有挑战性的任务。当前大多数抓握检测方法要么高度依赖于物体的几何信息，要么在受限配置内生成可行的抓握姿势。在这封信中，提出了一种抓取姿势检测框架，可以生成一组丰富的高精度 6-DoF 抓取姿势。首先，设计了一种具有多半径圆柱采样的新颖特征融合模块来增强局部几何表示。其次，开发了优化的抓取操作头以进一步估计抓取参数。最后，提出了一种抓取姿势传播算法，该算法有效地将抓取姿势从受限配置扩展到更大的配置。在大规模基准 GraspNet-1Billion 上的实验表明，所提出的方法优于现有方法（+8.61 AP）。现实世界的实验进一步证明了该方法在杂乱环境中的有效性。