A Vision-based Robotic Grasping System Using Deep Learning for 3D Object Recognition and Pose Estimation
文献类型：会议论文
| 作者 | Jincheng Yu; Kaijian Weng; Guoyuan Liang; Guanghan Xie |
| 出版日期 | 2013 |
| 会议名称 | 2013 IEEE International Conference on Robotics and Biomimetics, ROBIO 2013 |
| 会议地点 | Shenzhen, China |
| 英文摘要 | Pose estimation of object is one of the key problems for the automatic-grasping task of robotics. In this paper, we present a new vision-based robotic grasping system, which can not only recognize different objects but also estimate their poses by using a deep learning model, finally grasp them and move to a predefined destination. The deep learning model demonstrates strong power in learning hierarchical features which greatly facilitates the recognition mission. We apply the Max-pooling Convolutional Neural Network (MPCNN), one of the most popular deep learning models, in this system, and assign different poses of objects as different classes in MPCNN. Besides, a new object detection method is also presented to overcome the disadvantage of the deep learning model. We have built a database comprised of 5 objects with different poses and illuminations for experimental performance evaluation. The experimental results demonstrate that our system can achieve high accuracy on object recognition as well as pose estimation. And the vision-based robotic system can grasp objects successfully regardless of different poses and illuminations. |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/4624]  |
| 专题 | 深圳先进技术研究院_集成所 |
| 作者单位 | 2013 |
| 推荐引用方式 GB/T 7714 | Jincheng Yu,Kaijian Weng,Guoyuan Liang,et al. A Vision-based Robotic Grasping System Using Deep Learning for 3D Object Recognition and Pose Estimation[C]. 见:2013 IEEE International Conference on Robotics and Biomimetics, ROBIO 2013. Shenzhen, China. |
入库方式： OAI收割
来源：深圳先进技术研究院
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