论文标题
使用触点进行软跟踪，以进行混乱的对象进行盲物体检索
Soft Tracking Using Contacts for Cluttered Objects to Perform Blind Object Retrieval
论文作者
论文摘要
从混乱的空间中检索一个物体，例如橱柜，冰箱或垃圾箱，需要跟踪具有有限或没有视觉感应的物体。在这些情况下，需要接触反馈来估计对象的姿势，但是对象是可移动的，而它们的形状和数字可能未知，这使得与对象的联系非常困难。虽然先前的工作集中在多目标跟踪上，但其中的假设禁止使用先前的方法给定接触感应方式。取而代之的是，本文提出了使用杂物对象（Stucco）的触点进行软跟踪的方法，该对象（Stucco）跟踪了使用粒子滤波器对触点位置和隐式对象关联的信念。此方法允许在新信息可用时修改过去联系人的对象关联。我们将灰泥应用于盲物体检索问题，其中已知形状但必须从混乱中检索未知姿势的目标对象。我们的结果表明，我们的方法在四个仿真环境中以及在接触感应的真实机器人上优于基准。在仿真中，我们在所有环境中至少获得了至少65％的成功，而没有基准的实现超过5％。
Retrieving an object from cluttered spaces suchas cupboards, refrigerators, or bins requires tracking objects with limited or no visual sensing. In these scenarios, contact feedback is necessary to estimate the pose of the objects, yet the objects are movable while their shapes and number may be unknown, making the association of contacts with objects extremely difficult. While previous work has focused on multi-target tracking, the assumptions therein prohibit using prior methods given only the contact-sensing modality. Instead, this paper proposes the method Soft Tracking Using Contacts for Cluttered Objects (STUCCO) that tracks the belief over contact point locations and implicit object associations using a particle filter. This method allows ambiguous object associations of past contacts to be revised as new information becomes available. We apply STUCCO to the Blind Object Retrieval problem, where a target object of known shape but unknown pose must be retrieved from clutter. Our results suggest that our method outperforms baselines in four simulation environments, and on a real robot, where contact sensing is noisy. In simulation, we achieve grasp success of at least 65% on all environments while no baselines achieve over 5%.
