Learning spatial relationships from 3D vision using histograms
Learning spatial relationships from 3D vision using histograms
复制标题
使用直方图从 3D 视觉学习空间关系
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
Frank Guerin
中科院分区：
文献类型：
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作者：
Severin Fichtl;Andrew McManus;Wail Mustafa;D. Kraft;N. Krüger;Frank Guerin
有效的机器人操作需要一个能够提取环境特征的视觉系统，这些特征决定了哪些操作动作是可能的。在承认“负担能力”的大旗帜下，已有这方面的现有工作。我们特别感兴趣的是对象对之间的关系所提供的动作的可能性。例如，如果一个对象在另一个对象的“内部”或“在另一个对象的顶部”。为此，需要一种能够识别场景中的这种关系的视觉系统。我们使用的方法是视觉系统首先分割图像，然后考虑一对对象来确定它们的物理关系。该系统提取分割图像中每个对象的表面片，然后通过查看一个对象的表面片和另一个对象的表面片之间的关系来编辑各种直方图。从这些直方图训练分类器来识别一对对象之间的关系。我们的结果确定了构建直方图的最有希望的方法，以便能够以高精度对物理关系进行分类。这项工作对于机械手机器人来说很重要，他们可能会看到新的场景，必须识别突出的物理关系才能规划操作活动。
Effective robot manipulation requires a vision system which can extract features of the environment which determine what manipulation actions are possible. There is existing work in this direction under the broad banner of recognising “affordances”. We are particularly interested in possibilities for actions afforded by relationships among pairs of objects. For example if an object is “inside” another or “on top” of another. For this there is a need for a vision system which can recognise such relationships in a scene. We use an approach in which a vision system first segments an image, and then considers a pair of objects to determine their physical relationship. The system extracts surface patches for each object in the segmented image, and then compiles various histograms from looking at relationships between the surface patches of one object and those of the other object. From these histograms a classifier is trained to recognise the relationship between a pair of objects. Our results identify the most promising ways to construct histograms in order to permit classification of physical relationships with high accuracy. This work is important for manipulator robots who may be presented with novel scenes and must identify the salient physical relationships in order to plan manipulation activities.