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arXiv 2201.10434cs.RO
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基于接触的杂波目标软跟踪盲目标检索

Soft Tracking Using Contacts for Cluttered Objects to Perform Blind Object Retrieval

University of Michigan（密歇根大学）

Sheng Zhong, Nima Fazeli, Dmitry Berenson

备注：
8 pages, 7 figures, 2 tables, and 3 algorithm blocks, accepted to RA-L, preprint
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摘要：

从橱柜、冰箱或垃圾箱等杂乱的空间中检索对象需要用有限的视觉传感或没有视觉传感来跟踪对象。在这些场景中，接触反馈对于估计对象的姿态是必要的，然而对象是可移动的，而它们的形状和数量可能是未知的，使得接触与对象的关联极其困难。虽然以前的工作集中在多目标跟踪，其中的假设禁止使用现有的方法只给出了接触式传感模式。相反，本文提出的方法软跟踪使用接触杂乱的对象（STUCCO），跟踪的信念接触点定位和隐式对象关联使用粒子滤波器。这种方法允许在新信息可用时修改过去联系人的模糊对象关联。我们应用STUCCO盲对象检索问题，其中已知形状，但未知的姿态的目标对象必须从杂波中检索。我们的研究结果表明，我们的方法优于基线在四个模拟环境中，并在一个真正的机器人，接触传感是嘈杂的。在模拟中，我们在所有环境中实现了至少65%的抓取成功率，而没有基线达到5%以上。

英文摘要：

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%.

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。

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