基于触觉和视觉的吸持物插入实时状态估计
Realtime State Estimation with Tactile and Visual Sensing for Inserting a Suction-held Object
摘要：
我们开发了一个实时的状态估计系统来恢复的姿态和接触形成的对象相对于它的环境。在本文中，我们专注于将吸盘拾取的物体插入到狭小空间中的应用，这是机器人包装的一项使能技术。我们提出了一个框架，融合力和视觉感知，以提高准确性和鲁棒性。视觉感测是通用的和非侵入性的，但是遭受遮挡和有限的准确性，特别是对于涉及接触的任务。触觉感知是局部的，但提供了对闭塞的准确性和鲁棒性。所提出的算法融合它们是基于iSAM，一种在线优化技术，我们使用它来结合从机器人的运动学测量，接触的对象和容器的几何形状，和视觉跟踪。在本文中，我们推广以前的结果在平面设置到一个3D的任务，更复杂的接触相互作用。使用力感测的一个关键挑战是我们不能直接观察接触点定位。我们提出了一个数据驱动的方法来推断接触形成，然后使用实时的状态估计。我们演示和评估的算法在一个安装仪表提供地面实况。
英文摘要：
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, an enabling technology for robotic packaging. We propose a framework that fuses force and visual sensing for improved accuracy and robustness. 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 optimization 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 to a 3D task with more complex contact interactions. A key challenge in using force sensing is that we do not observe contact point locations 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
