NDLI: A vision-based robotic grasping system using deep learning for 3D object recognition and pose estimation

Content Provider	IEEE Xplore Digital Library
Author	Jincheng Yu Kaijian Weng Guoyuan Liang Guanghan Xie
Copyright Year	2013
Description	Author affiliation: Guangdong Univ. of Technol., Guangzhou, China (Guanghan Xie) \|\| Guangdong Provincial Key Lab. of Robot. & Intell. Syst., Shenzhen Inst. of Adv. Technol., Shenzhen, China (Jincheng Yu; Kaijian Weng; Guoyuan Liang)
Abstract	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.
Sponsorship	IEEE Robot. Autom. Soc.
Starting Page	1175
Ending Page	1180
File Size	1273430
Page Count	6
File Format	PDF
ISBN	9781479927449
DOI	10.1109/ROBIO.2013.6739623
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-12
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Estimation Robots Grasping Object recognition Object detection Databases Training
Content Type	Text
Resource Type	Article

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