This paper introduces a novel approach for the grasping and precise placement of various known rigid objects using multiple grippers within highly cluttered scenes. Using a single depth image of the scene, our method estimates multiple 6D object poses together with an object class, a pose distance for object pose estimation, and a pose distance from a target pose for object placement for each automatically obtained grasp pose with a single forward pass of a neural network. By incorporating model knowledge into the system, our approach has higher success rates for grasping than state-of-the-art model-free approaches. Furthermore, our method chooses grasps that result in significantly more precise object placements than prior model-based work.
翻译:本文引入了一种新颖的方法,在高度杂乱的场景中运用多重抓抓器来捕捉和精确定位各种已知的僵硬物体。 使用现场的单一深度图像,我们的方法估计了多个6D对象与一个物体类别一起构成,物体构成估计的距离,以及每个自动获得的抓住物体的定位目标与神经网络的单一前方传球之间的距离。 通过将模型知识纳入系统,我们的方法在捕捉上的成功率高于最先进的无模型方法。 此外,我们的方法选择了比以前基于模型的工作更精确得多的捕捉。