Detecting Grasping Positions and Postures in 3D Point Clouds by Geometric Constraints

2019 IEEE International Conference on Unmanned Systems (ICUS) Pub Date : 2019-10-01 DOI:10.1109/ICUS48101.2019.8996056

Shiming Zhao, Yingqiu Xu, Yingzi Tan

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引用次数: 1

Abstract

In this paper, a method of positioning grasping points and grasping posture on strange objects by using geometric constraints is proposed. The input to the algorithm is point clouds of the objects and the geometric parameters of the gripper of the manipulator. The output of the algorithm is a set of grasping points and grasping posture, which can be expected to be the best target for the manipulator to grasp the object. The algorithm first determines the grasping geometric parameters of the gripper, and then sets a series of necessary conditions for grasping the objects successfully. After that, a large number of grasp point samples are carried out on the point cloud and the necessary conditions are used for filtering to obtain a set of potential grasping points. Finally, a kind of weighted calculation method is used to obtain the best grasps. The algorithm does not need to know the objects in advance, and it can get a better grasping effect on some objects with special shape (such as ring). The algorithm is provided as a ROS package for download at https://github.com/ZSM2019/Geometry_Grasp.

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利用几何约束检测三维点云中的抓取位置和姿态

提出了一种利用几何约束对陌生物体进行抓取点和抓取姿态定位的方法。该算法的输入是物体的点云和机械手夹持器的几何参数。该算法的输出是一组抓取点和抓取姿态，可以预期这是机械手抓取物体的最佳目标。该算法首先确定抓取器的抓取几何参数，然后设置成功抓取物体的一系列必要条件。然后，对点云进行大量抓取点样本，并根据必要条件进行滤波，得到一组潜在的抓取点。最后，采用一种加权计算方法获得最佳抓地力。该算法不需要事先了解物体，对一些形状特殊的物体(如圆环)可以获得较好的抓取效果。该算法以ROS包的形式提供，可从https://github.com/ZSM2019/Geometry_Grasp下载。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2019 IEEE International Conference on Unmanned Systems (ICUS)

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