摘要翻译:
给定一个点云,我们考虑推断三维关节物体如盒子的运动学模型,以便对它们进行操作。虽然以前的工作已经展示了如何提取平面运动学模型(通常表示为线性链),但这种平面模型不适用于由通常以循环配置连接到其他段的段组成的3D对象。我们提出了一种建立模型的方法,该方法能够捕捉输入点云特征与目标段之间的关系以及相邻目标段之间的关系。我们使用一个条件随机场,允许我们对对象的不同段之间的依赖关系进行建模。我们测试了我们的方法从局部和噪声点云数据推断运动学结构,用于各种各样的盒子,包括蛋糕盒,比萨饼盒,和几个大小的纸板盒。推断出的结构使我们的机器人能够通过操纵襟翼成功地关闭这些盒子。
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英文标题:
《Inferring 3D Articulated Models for Box Packaging Robot》
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作者:
Heran Yang, Tiffany Low, Matthew Cong, Ashutosh Saxena
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最新提交年份:
2011
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分类信息:
一级分类:Computer Science 计算机科学
二级分类:Robotics 机器人学
分类描述:Roughly includes material in ACM Subject Class I.2.9.
大致包括ACM科目I.2.9类的材料。
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一级分类:Computer Science 计算机科学
二级分类:Artificial Intelligence 人工智能
分类描述:Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域,除了视觉、机器人、机器学习、多智能体系统以及计算和语言(自然语言处理),这些领域有独立的学科领域。特别地,包括专家系统,定理证明(尽管这可能与计算机科学中的逻辑重叠),知识表示,规划,和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类:Computer Science 计算机科学
二级分类:Computer Vision and Pattern Recognition 计算机视觉与模式识别
分类描述:Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
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英文摘要:
Given a point cloud, we consider inferring kinematic models of 3D articulated objects such as boxes for the purpose of manipulating them. While previous work has shown how to extract a planar kinematic model (often represented as a linear chain), such planar models do not apply to 3D objects that are composed of segments often linked to the other segments in cyclic configurations. We present an approach for building a model that captures the relation between the input point cloud features and the object segment as well as the relation between the neighboring object segments. We use a conditional random field that allows us to model the dependencies between different segments of the object. We test our approach on inferring the kinematic structure from partial and noisy point cloud data for a wide variety of boxes including cake boxes, pizza boxes, and cardboard cartons of several sizes. The inferred structure enables our robot to successfully close these boxes by manipulating the flaps.
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PDF链接:
https://arxiv.org/pdf/1106.4632