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Volume: 30 | Article ID: art00018
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Reliable Primitive Approximation for Estimation of Robot Grasping Parameters Using 3D-DNN
  DOI :  10.2352/ISSN.2470-1173.2018.09.IRIACV-301  Published OnlineJanuary 2018
Abstract

In automatic picking by robot, it is the important to estimate the grasping parameters (grasping position, direction and angle) of the object. In this paper, we propose a method for approximating an object with primitive shape in order to estimate the grasping parameters. The basic idea of this research is to approximate the object by object primitive (hexahedron/cylinder/sphere), based on the object's surface. First, we classify the surface shape that constitutes the object using 3D-Deep Neural Network. Then, we approximate the object with object primitive using the recognition result of 3D-DNN. After that, we estimate the grasping parameters based on preset grasping rules. The success rate of approximating the object primitive with our method was 94.7%. This result is 6.7% higher than the 3D ShapeNets using 3D-DNN. Also, as an experimental result of grasping simulation using Gazebo, the success rate of grasping with our method was 85.6%.

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Takuya Torii, Manabu Hashimoto, "Reliable Primitive Approximation for Estimation of Robot Grasping Parameters Using 3D-DNNin Proc. IS&T Int’l. Symp. on Electronic Imaging: Intelligent Robotics and Industrial Applications using Computer Vision,  2018,  pp 301-1 - 301-6,  https://doi.org/10.2352/ISSN.2470-1173.2018.09.IRIACV-301

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