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

2013 IEEE International Conference on Robotics and Biomimetics, ROBIO 2013

Volumn , Issue , 2013, Pages 1175-1180

  Yu, Jincheng ^a   Weng, Kaijian ^a   Liang, Guoyuan ^a   Xie, Guanghan ^b  

^a SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY   (China)

^b GUANGDONG UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

BIOMIMETICS; END EFFECTORS; NEURAL NETWORKS; OBJECT RECOGNITION; ROBOTICS;

3D OBJECT RECOGNITION; CONVOLUTIONAL NEURAL NETWORK; EXPERIMENTAL PERFORMANCE EVALUATIONS; HIERARCHICAL FEATURES; OBJECT DETECTION METHOD; POSE ESTIMATION; ROBOTIC GRASPING; ROBOTIC SYSTEMS;

GESTURE RECOGNITION;

EID: 84898822427     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ROBIO.2013.6739623     Document Type: Conference Paper

References (14)

1. A. Collet, M. Martinez, and S. S. Srinivasa, "The MOPED framework: Object Recognition and Pose Estimation for Manipulation", in International Journal of Robotics Research, 2011.
2. Fenzi M, Dragon R, Leal-Taix L, "3D Object Recognition and Pose Estimation for Multiple Objects using Multi-Prioritized RANSAC and Model Updating", in Pattern Recognition, 2012, pp. 123-133.
3. Bhat, S., Berger, M. O., Sur, F., "Visual Words for 3D Reconstruction and Pose Computation", in 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT), 2011, pp. 326-333.
4. E. Klingbeil, D. Drao, B. Carpenter, V. Ganapathi, O. Khatib, and A. Y. Ng, "Grasping with application to an autonomous checkout robot", in Proc. IEEE Int. Conf. Robotics and Automation (ICRA), 2011.
5. K. Hbner, D. Kragic, "Selection of robot pre-grasps using box-based shape approximation",in IEEE Int. Conference on Intelligent Robots and Systems, 2008, pp. 1765-1770.
6. Mila Popovic, Gert Kootstra, Jimmy A. Jrgensen, Danica Kragic, Norbert Krger, "Grasping unknown objects using an early cognitive vision system for general scene understanding", in Proceedings of the International Conference on Intelligent Robots and Systems (IROS), 2011, pp. 987-994.
7. Ciresan, D. C., Meier, U., Schmidhuber, J., "Multi-column deep neural networks for image classification", in Computer Vision and Pattern Recognition, 2012, pp. 3642-3649.
8. LeCun, Y., Huang, F. J., Bottou, L., "Learning methods for generic object recognition with invariance to pose and lighting", in Computer Vision and Pattern Recognition, 2004, pp. 97-104.
9. Z. Zhang, "A flexible new technique for camera calibration", IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, pp. 1330-1334.
10. H. Zha, C. Ding, M. Gu, X. He, and H. Simon, "Spectral relaxation for k-means clustering", in Advances in Neural Information Processing Systems, 2002.
11. D. H. Hubel and T. N. Wiesel, "Receptive fields and functional architecture of monkey striate cortex",in Journal of Physiology, 1968, pp. 215-243.
12. D. Scherer, A. Muller, and S. Behnke, "Evaluation of pooling operations in convolutional architectures for object recognition", in Proc. of the Intl. Conf. on Artificial Neural Networks, 2010, pp. 92-101.
13. Kevin Jarrett, Koray Kavukcuoglu, Marc'Aurelio Ranzato and Yann LeCun, "What is the Best Multi-Stage Architecture for Object Recognition?", in Proc. International Conference on Computer Vision (ICCV), 2009.
14. Gou, Zhijian, Ying Sun, and Haiying Yu, "Inverse kinematics equation of 6-DOF robot based on geometry projection and simulation", in Computer, Mechatronics, Control and Electronic Engineering (CMCE), 2010, pp. 125-128