装箱机器人三维关节模型的推导
Inferring 3D Articulated Models for Box Packaging Robot
摘要：
给定一个点云，我们考虑推断3D铰接对象的运动学模型，如框的目的是操纵它们。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链接到其他段的段组成的3D对象。我们提出了一种方法来建立一个模型，捕捉输入点云特征和对象段之间的关系，以及相邻对象段之间的关系。我们使用一个条件随机场，使我们能够对对象的不同部分之间的依赖关系进行建模。我们测试我们的方法推断运动学结构的部分和噪声点云数据的各种各样的盒子，包括蛋糕盒，比萨饼盒，和纸板箱的几个大小。推断出的结构使我们的机器人能够通过操纵襟翼成功关闭这些盒子。
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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