Reasoning simplified volumetric shapes for robotic grasping

Jae-Moon Chung, T. Nagata
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引用次数: 6

Abstract

This paper presents a method for reasoning volumetric shapes for robotic grasping based on a data-driven approach. The global information about position, orientation and geometrical property of objects is necessary for successful execution of the target approach and the preparation, at the early stage of grasping, rather than fine details of the shapes. A pair of occluding contours is used to extract a simplified 3D shape of circular generalized cylinders (GCs) holding the information. Based on the GC model, which is represented by a family of spheres of varying radii with their centers on its axis, the stereo matching between the contour pair is implemented using the geometrical invariance of spheres. The shapes of blobs and sticks can be simplified as spheres and the families of spheres, respectively. Compared with the range-data based method, its computation is inexpensive, directive and has unique solution. The case of multiple and overlap objects is also addressed.
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推理简化了机器人抓取的体积形状
本文提出了一种基于数据驱动方法的机器人抓取体积形状推理方法。在抓取的早期阶段,成功地执行目标逼近和准备需要的是关于物体的位置、方向和几何属性的全局信息,而不是形状的精细细节。使用一对遮挡轮廓提取包含信息的广义圆柱体的简化三维形状。在GC模型的基础上,利用球的几何不变性实现了等高线对之间的立体匹配,该模型由一组半径不同的球体表示,球体的中心在其轴上。团和棒的形状可以分别简化为球体和球体族。与基于距离数据的方法相比,该方法具有计算成本低、指导性强、解唯一等优点。还讨论了多个和重叠对象的情况。
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