For grasp network algorithms, generating grasp datasets for a large number of 3D objects is a crucial task. However, generating grasp datasets for hundreds of objects can be very slow and consume a lot of storage resources, which hinders algorithm iteration and promotion. For point cloud grasp network algorithms, the network input is essentially the internal point cloud of the grasp area that intersects with the object in the gripper coordinate system. Due to the existence of a large number of completely consistent gripper area point clouds based on the gripper coordinate system in the grasp dataset generated for hundreds of objects, it is possible to remove the consistent gripper area point clouds from many objects and assemble them into a single object to generate the grasp dataset, thus replacing the enormous workload of generating grasp datasets for hundreds of objects. We propose a new approach to map the repetitive features of a large number of objects onto a finite set.To this end, we propose a method for extracting the gripper area point cloud that intersects with the object from the simulator and design a gripper feature filter to remove the shape-repeated gripper space area point clouds, and then assemble them into a single object. The experimental results show that the time required to generate the new object grasp dataset is greatly reduced compared to generating the grasp dataset for hundreds of objects, and it performs well in real machine grasping experiments. We will release the data and tools after the paper is accepted.
翻译:对于抓取网络算法来说,生成大量三维物体的抓取数据集是一项关键任务。然而,为数百个对象生成抓取数据集可能非常缓慢,消耗大量存储资源,这妨碍了算法的迭代和推广。对于点云抓取网络算法,网络输入本质上是在夹持手坐标系内与对象相交的抓取区域的内部点云。由于为数百个物体生成的抓取数据集中存在大量完全一致的基于夹持手坐标系的夹持器区域点云,因此有可能将许多对象中的一致夹持器区域点云移除并把它们拼合成一个单一的对象来生成抓取数据集,从而代替为数百个物体生成抓取数据集的巨大工作量。我们提出了一种新的方法来将大量物体的重复特征映射到一个有限集合上。为此,我们提出了一种从模拟器中提取与对象相交的抓取区域点云的方法,并设计了一个夹持器特征过滤器来移除形状相同的夹持空间区域点云,然后将它们拼合成一个单一的对象。实验结果表明,与为数百个物体生成抓取数据集相比,生成新对象抓取数据集所需的时间大大缩短,并在实际机器抓取实验中表现出良好的性能。我们将在论文被接受后发布数据和工具。