3D Pose Estimation for Robotic Grasping Using Deep Convolution Neural Network

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	3D Pose Estimation for Robotic Grasping Using Deep Convolution Neural Network
	Zhang, Yafang 3; Sun Z(孙振)3; Zhang XH(张晓会)i 3; Wang JC(王君臣)2,3; Xu Y(徐颖)3; Wang, Yao 3; Song GL(宋国立)1
	2018
会议日期	December 12-15, 2018
会议地点	Kuala Lumpur, Malaysia
页码	513-517
英文摘要	With the progress of artificial intelligence, robots begin to enter family service. Autonomous object grasping in a cluttered scene is the most frequent operation of a service robot in daily life while it is still a challenging problem in the field of robotics. In this paper, we develop a robot system using a deep convolution neural network for 3D object grasping. The system is composed by a color camera, and a robot arm with a gripper. The color camera provides robotic vision about surrounding environments; the deep neural network performs an end-to-end mapping from vision images to the 3D poses of the object of interest; the robot arm with the gripper is then driven to grasp the object. In addition, we also present an automatic data labeling method for the training of the convolution neural network. Preliminary experiments were performed to evaluate our robot system and the results have confirmed its effectiveness.
产权排序	3
会议录	Proceedings of the 2018 IEEE International Conference on Robotics and Biomimetics
会议录出版者	IEEE
会议录出版地	New York
语种	英语
ISBN号	978-1-7281-0376-1
WOS记录号	WOS:000468772200081
内容类型	会议论文
源URL	[http://ir.sia.cn/handle/173321/24658]
专题	沈阳自动化研究所_机器人学研究室
通讯作者	Wang JC(王君臣)
作者单位	1.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, State Key Laboratory of Robotics, Shenyang Institute of Automation, Shenyang 110016, China 2.Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing 100083, China 3.School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China
推荐引用方式 GB/T 7714	Zhang, Yafang,Sun Z,Zhang XH,et al. 3D Pose Estimation for Robotic Grasping Using Deep Convolution Neural Network[C]. 见:. Kuala Lumpur, Malaysia. December 12-15, 2018.