Inferring Occluded Geometry Improves Performance when Retrieving an Object from Dense Clutter
Inferring Occluded Geometry Improves Performance when Retrieving an Object from Dense Clutter
复制标题
从密集的杂乱中检索对象时，推断被遮挡的几何图形可提高性能
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
D. Berenson
中科院分区：
文献类型：
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作者：
A. Price;Linyi Jin;D. Berenson
对象搜索-在杂乱场景中找到目标对象的问题-对于解决仓库和家庭环境中的许多机器人应用至关重要。然而，杂乱的环境需要对象经常彼此遮挡，使得难以分割对象并推断其形状和属性。我们不依赖于CAD或场景对象的其他显式模型的可用性，而是使用最先进的深度神经网络来完成形状以及体积记忆系统来增强杂乱环境的操纵规划器，从而使机器人能够推理遮挡区域中可能包含的内容。我们测试的系统在各种桌面操作场景组成的家庭用品，突出其适用性现实领域。我们的研究结果表明，将这两个组件到一个操纵规划框架显着减少了在密集的混乱中找到一个隐藏的对象所需的行动的数量。
Object search -- the problem of finding a target object in a cluttered scene -- is essential to solve for many robotics applications in warehouse and household environments. However, cluttered environments entail that objects often occlude one another, making it difficult to segment objects and infer their shapes and properties. Instead of relying on the availability of CAD or other explicit models of scene objects, we augment a manipulation planner for cluttered environments with a state-of-the-art deep neural network for shape completion as well as a volumetric memory system, allowing the robot to reason about what may be contained in occluded areas. We test the system in a variety of tabletop manipulation scenes composed of household items, highlighting its applicability to realistic domains. Our results suggest that incorporating both components into a manipulation planning framework significantly reduces the number of actions needed to find a hidden object in dense clutter.
影响因子：
3.5
作者：
Hornung, Armin;Wurm, Kai M.;Burgard, Wolfram
通讯作者：
Burgard, Wolfram