1. bookVolume 23 (2015): Edizione s1 (August 2015)
    Edizione Title: Special number
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eISSN
1338-0532
Prima pubblicazione
03 Mar 2011
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2 volte all'anno
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Improving Cognitive Skills of the Industrial Robot

Pubblicato online: 30 Sep 2015
Volume & Edizione: Volume 23 (2015) - Edizione s1 (August 2015) - Edizione Title: Special number
Pagine: 19 - 28
Dettagli della rivista
License
Formato
Rivista
eISSN
1338-0532
Prima pubblicazione
03 Mar 2011
Frequenza di pubblicazione
2 volte all'anno
Lingue
Inglese

1. OpenCV: http://opencv.org/, (Accessed: Apr. 24, 2013).Search in Google Scholar

2. Point Cloud Library (PCL), http://www.pointclouds.org/, (Accessed: Apr. 24, 2013).Search in Google Scholar

3. O. SKOTHEIM, J. T. THIELEMANN, A. BERGE, AND A. SOMMERFELT. 2010. Robust 3D object localization and pose estimation for random bin picking with the 3DMaMa algorithm. Instrumentation.10.1117/12.838796Search in Google Scholar

4. R. BLOSS. 2006. Smart robot that picks parts from bins. Assembly Automation, Vol. 26, No. 4, pp. 279-282.Search in Google Scholar

5. K. BOEHNKE. 2007. Object localization in range data for robotic bin picking. In: Automation Science and Engineering, 2007. CASE 2007. IEEE International Conference on, 2007, pp. 572-577.Search in Google Scholar

6. K. BÖHNKE and A. GOTTSCHEBER. 2010. Fast Object Registration and Robotic Bin Picking. In: Research and Education in Robotics-EUROBOT 2009, pp. 23-37.Search in Google Scholar

7. M.-Y. LIU, O. TUZEL, A. VEERARAGHAVAN, Y. TAGUCHI, T. K. MARKS and R. CHELLAPPA. 2012. Fast object localization and pose estimation in heavy clutter for robotic bin picking. The International Journal of Robotics Research, Vol. 31, No. 8, pp. 951-973.Search in Google Scholar

8. S. SAVARESE and L. FEI-FEI. 2007. 3D generic object categorization, localization and pose estimation. In: IEEE 11th International Conference on Computer Vision.10.1109/ICCV.2007.4408987Search in Google Scholar

9. J. STURM, K. KONOLIGE, C. STACHNISS, and W. BURGARD. 2010. 3D Pose Estimation, Tracking and Model Learning of Articulated Objects from Dense Depth Video using Projected Texture Stereo. In: Proc. of the Workshop RGB-D: Advanced Reasoning with Depth Cameras at Robotics: Science and Systems (RSS).10.1109/ROBOT.2010.5509796Search in Google Scholar

10. S. MAY, D. DROESCHEL, D. HOLZ, C. WIESEN and S. FUCHS. 2008. 3D pose estimation and mapping with time-of-flight cameras. In: International Conference on Intelligent Robots and Systems (IROS), 3D Mapping workshop. Nice, France.10.1109/IROS.2009.5354684Search in Google Scholar

11. V. LEPETIT, J. PILET, and P. FUA. 2004. Point matching as a classification problem for fast and robust object pose estimation. In Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2004, Vol. 2, pp. 244-250.Search in Google Scholar

12. M. VILLAMIZAR et al. 2011. Efficient 3D Object Detection using Multiple Pose-Specific Classifiers. In: Proceedings British Machine Vision Conference (BMVC).10.5244/C.25.20Search in Google Scholar

13. B. M. PLANITZ, A. J. MAEDER, and J. A. WILLIAMS. 2005. The correspondence framework for 3D surface matching algorithms. Computer Vision and Image Understanding, Vol. 97, No. 3, pp. 347-383.Search in Google Scholar

14. P. J. BESL and H. D. McKAY. 1992. A method for registration of 3-D shapes. IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 14, pp. 239-256.Search in Google Scholar

15. M. KAZHDAN and T. FUNKHOUSER. 2002. Harmonic 3D shape matching. Int’l Conf. on Computer Graphics and Interactive Techniques, p. 191.Search in Google Scholar

16. C. DORAI and A. K. JAIN. 1997. COSMOS-A representation scheme for 3d free-form object. IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 19, No. 10, pp. 1115-1130.Search in Google Scholar

17. A. E. JOHNSON and M. HEBERT. 1999. Using spin images for efficient object recognition in cluttered 3D scenes. IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 21, No. 5, pp. 433-449.Search in Google Scholar

18. R. B. RUSU, Z. C. MARTON, N. BLODOW, and M. BEETZ. 2008. Learning informative point classes for the acquisition of object model maps. In 2008 10th International Conference on Control, Automation, Robotics and Vision, pp. 643-650.Search in Google Scholar

19. R. B. RUSU, A. HOLZBACH, N. BLODOW, and M. BEETZ. 2009. Fast geometric point labeling using conditional random fields. In: 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 7-12.Search in Google Scholar

20. C.-S. CHEN, Y.-P. HUNG, and J.-B. CHENG. 1999. RANSAC-based DARCES: A New Approach to Fast Automatic Registration of Partially Overlapping Range Images. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 21, No. 11, pp. 1229-1234.Search in Google Scholar

21. J. PAPON, A. ABRAMOV, M. SCHOELER, and F. WORGOTTER. 2013. Voxel Cloud Connectivity Segmentation - Supervoxels for Point Clouds. IEEE Conference on CVPR 2013. Portland, Oregon.10.1109/CVPR.2013.264Search in Google Scholar

22. R. O. TSAI and R. K. LENZ, “A new technique for fully autonomous and efficient 3D robotics hand/eye calibration,” IEEE Transactions on Robotics and Automation, Vol. 5, No. 3, 1989.10.1109/70.34770Search in Google Scholar

23. J. K. LEE, K. KIM, Y. LEE, and T. JEONG. 2011. Simultaneous Intrinsic and Extrinsic Parameter Identification of a Hand-Mounted Laser-Vision Sensor. Sensor, Vol. 11, pp. 8751-8768.Search in Google Scholar

24. Q. ZHANG and R. PLESS. 2004. Extrinsic Calibration of a Camera and Laser Range Finder (improves camera calibration). International conference on Intelligent Robots and Systems, pp. 2302-2306. Sendia, Japan. G.-Q. WEI and G. HIRZINGER. 1998. Active Self-Calibration of Hand-Mounted Laser Range Finders. IEEE Transactions on Robotics and Automation, Vol. 14, No. 3, pp. 493-497.Search in Google Scholar

25. Y. JINCHENG, K. WENG, G. LIANG, G. XIE. 2013. A vision-based robotic grasping system using deep learning for 3D object recognition and pose estimation. IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 1175-1180.Search in Google Scholar

26. J. NAGI, F. DUCATELLE, G.A. DI CARO, D. CIRESAN, U. MEIER, A. GIUSTI, F. NAGI, J. SCHMIDHUBER, L.M. GAMBARDELLA. 2011. Max-pooling convolutional neural networks for vision-based hand gesture recognition. IEEE International Conference on Signal and Image Processing Applications (ICSIPA), pp. 342-347. Search in Google Scholar

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