Occlusion-aware reconstruction and manipulation of 3D articulated objects
Occlusion-aware reconstruction and manipulation of 3D articulated objects
复制标题
3D 关节对象的遮挡感知重建和操作
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
Stan Birchfield
中科院分区：
文献类型：
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作者：
Xiaoxia Huang;I. Walker;Stan Birchfield
我们提出了一种方法来恢复完整的三维模型的铰接对象。结构-从运动技术是用来捕捉三维点云模型的对象在两种不同的配置。Procrustes分析和RANSAC的新组合促进了一种直接的几何方法来恢复关节轴，并自动将它们分类为旋转或棱镜。利用由此产生的关节模型，机器人系统能够沿着其关节轴在指定的抓点上操纵物体，以行使其自由度。由于模型捕获了物体的所有侧面，因此它们具有遮挡意识，使机器人系统能够规划到当前视图中不可见的物体部分的路径。我们的算法不需要物体的先验知识，也不需要对物体或场景的平面度做任何假设。以PUMA 500机械臂为实验对象，验证了该方法在具有转动关节和移动关节的各种物体上的有效性。
We present a method to recover complete 3D models of articulated objects. Structure-from-motion techniques are used to capture 3D point cloud models of the object in two different configurations. A novel combination of Procrustes analysis and RANSAC facilitates a straightforward geometric approach to recovering the joint axes, as well as classifying them automatically as either revolute or prismatic. With the resulting articulated model, a robotic system is able to manipulate the object along its joint axes at a specified grasp point in order to exercise its degrees of freedom. Because the models capture all sides of the object, they are occluded-aware, enabling the robotic system to plan paths to parts of the object that are not visible in the current view. Our algorithm does not require prior knowledge of the object, nor does it make any assumptions about the planarity of the object or scene. Experiments with a PUMA 500 robotic arm demonstrate the effectiveness of the approach on a variety of objects with both revolute and prismatic joints.