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Realtime State Estimation with Tactile and Visual Sensing for Inserting a Suction-held Object

机译:插入吸持对象的触觉和视觉实时状态估计

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We develop a real-time state estimation system to recover the pose and contact formation of an object relative to its environment. In this paper, we focus on the application of inserting an object picked by a suction cup into a tight space, a key technology for robotic packaging. We propose a framework that fuses tactile and visual sensing. Visual sensing is versatile and non-intrusive, but suffers from occlusions and limited accuracy, especially for tasks involving contact. Tactile sensing is local, but provides accuracy and robustness to occlusions. The proposed algorithm to fuse them is based on iSAM, an on-line estimation technique, which we use to incorporate kinematic measurements from the robot, contact geometry of the object and the container, and visual tracking. In this paper, we generalize previous results in planar settings [1] to a 3D task with more complex contact interactions. A key challenge is that we do not observe contact locations between the suction-held object and the container directly. We propose a data-driven method to infer the contact formation, which is then used in real-time by the state estimator. We demonstrate and evaluate the algorithm in a setup instrumented to provide groundtruth.
机译:我们开发了一种实时状态估计系统,以恢复物体相对于其环境的姿态和接触形成。在本文中,我们专注于将由吸盘拾取的物体插入狭窄空间的应用,狭窄空间是机器人包装的关键技术。我们提出了一个融合触觉和视觉感知的框架。视觉感测是通用的并且是非侵入性的,但是受遮挡和精度有限的困扰,特别是对于涉及接触的任务。触觉是局部的,但可以为遮挡提供准确性和鲁棒性。提出的融合它们的算法基于在线估计技术iSAM,我们使用它结合了来自机器人的运动学测量,对象与容器的接触几何形状以及视觉跟踪。在本文中,我们将先前在平面设置中的结果[1]推广到具有更复杂的接触相互作用的3D任务。一个关键的挑战是我们不能直接观察到被吸物体与容器之间的接触位置。我们提出了一种数据驱动的方法来推断接触的形成,然后由状态估计器实时使用它。我们在设置为提供真实性的设置中演示和评估该算法。

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