Skip to main content

Visual Assistance to an Advanced Mechatronic Platform for Pick and Place Tasks

  • Conference paper
  • 3192 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6425))

Abstract

Recent advances in mechanical and electronic engineering led to the building of more sophisticated mechatronic systems excelling in simplicity, reliability and versatility. On the contrary, the complexity of their parts necessitate integrated control systems along with advanced visual feedback. Generally, a vision system aims at bridging the gap between humans and machines in terms of providing to the latter information about what is perceived visually. This paper shows how the vision system of an advanced mechatronic framework named ACROBOTER is used for the localization of objects. ACROBOTER develops a new locomotion technology that can effectively be utilized in a workplace environment for manipulating small objects simultaneously. Its vision system is based on a multi-camera framework that is responsible for both finding patterns and providing their location in the 3D working space. Moreover, this work presents a novel method for recognizing objects in a scene and providing their spatial information.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Kouskouridas, R., Kyriakoulis, N., Chrysostomou, D., Belagiannis, V., Mouroutsos, S., Gasteratos, A.: The Vision System of the ACROBOTER Project. In: International Confernce on Intelligent Robotics and Applications, pp. 957–966 (2009)

    Google Scholar 

  2. Kouskouridas, R., Badekas, E., Gasteratos, A.: Simultaneous Visual Object Recognition and Position Estimation Using SIFT. In: Xie, M., Xiong, Y., Xiong, C., Liu, H., Hu, Z. (eds.) ICIRA 2009. LNCS, vol. 5928, pp. 866–875. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  3. Balaguer, C., Gimenez, A., Huete, A., Sabatini, A., Topping, M., Bolmsjo, G.: The MATS robot: service climbing robot for personal assistance. IEEE Robotics & Automation Magazine 13(1), 51–58 (2006)

    Article  Google Scholar 

  4. http://www.honda.co.jp/ASIMO/

  5. http://www.care-o-bot.de/english/

  6. Sato, T., Fukui, R., Morishita, H., Mori, T.: Construction of ceiling adsorbed mobile robots platform utilizing permanent magnet inductive traction method. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2004, vol. 1 (2004)

    Google Scholar 

  7. Vaish, V., Levoy, M., Szeliski, R., Zitnick, C., Kang, S.: Reconstructing occluded surfaces using synthetic apertures: Stereo, focus and robust measures. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2 (2006)

    Google Scholar 

  8. Zhang, J., McMillan, L., Yu, J., Hill, U.: Robust tracking and stereo matching under variable illumination. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1 (2006)

    Google Scholar 

  9. Zwicker, M., Vetro, A., Yea, S., Matusik, W., Pfister, H., Durand, F.: Resampling, antialiasing, and compression in multiview 3-D displays. IEEE Signal Processing Magazine 24(6), 88–96 (2007)

    Article  Google Scholar 

  10. Schweighofer, G.: Robust pose estimation from a planar target. IEEE Trans. Pattern Anal. Mach. Intell. 28(12), 2024–2030 (2006)

    Article  Google Scholar 

  11. Chandraker, M., Stock, C., Pinz, A.: Real-time camera pose in a room. In: Crowley, J.L., et al. (eds.) ICVS 2003. LNCS, vol. 2626, pp. 98–110. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  12. Xu, D., Li, Y.F.: A new pose estimation method based on inertial and visual sensors for autonomous robots. In: IEEE International Conference on Robotics and Biomimetics, Sanya, China, pp. 405–410 (2007)

    Google Scholar 

  13. Torralba, A., Oliva, A.: Depth estimation from image structure. IEEE Trans. Pattern Anal. Mach. Intell., 1226–1238 (2002)

    Google Scholar 

  14. Naplapntidis, L., Sirakoulis, G., Gasteratos, A.: Review of stereo vision algorithms: from software to hardware. International Journal of Optomechatronics 2(4), 435–462 (2008)

    Article  Google Scholar 

  15. Nister, D., Stewenius, H.: Scalable recognition with a vocabulary tree. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, New York, USA, pp. 2161–2168 (2006)

    Google Scholar 

  16. Sivic, J., Zisserman, A.: Video google: a text retrieval approach to object matching in videos. In: Proceedings of the Ninth IEEE International Conference on Computer Vision, Nice, France, pp. 1470–1477 (2003)

    Google Scholar 

  17. Liao, M., Wei, L., Chen, W.: A novel affine invariant feature extraction for optical recognition, vol. 3, pp. 1769–1773 (August 2007)

    Google Scholar 

  18. Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1615–1630 (2005)

    Article  Google Scholar 

  19. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)

    Article  Google Scholar 

  20. Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (surf). Comput. Vis. Image Underst. 110(3), 346–359 (2008)

    Article  Google Scholar 

  21. Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Gool, L.V.: A comparison of affine region detectors. Int. J. Comput. Vision 65(1-2), 43–72 (2005)

    Article  Google Scholar 

  22. Mikolajczyk, K., Schmid, C.: Scale & affine invariant interest point detectors. Int. J. Comput. Vision 60(1), 63–86 (2004)

    Article  Google Scholar 

  23. Ferrari, V., Tuytelaars, T., Van Gool, L.: Simultaneous object recognition and segmentation by image exploration. In: Proceedings of the 8th European Conference on Computer Vision, Prague, Czech Republic, pp. 40–54 (2004)

    Google Scholar 

  24. Moreels, P., Perona, P.: A probabilistic cascade of detectors for individual object recognition. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part III. LNCS, vol. 5304, pp. 426–439. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Kouskouridas, R., Gasteratos, A. (2010). Visual Assistance to an Advanced Mechatronic Platform for Pick and Place Tasks. In: Liu, H., Ding, H., Xiong, Z., Zhu, X. (eds) Intelligent Robotics and Applications. ICIRA 2010. Lecture Notes in Computer Science(), vol 6425. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16587-0_64

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-16587-0_64

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-16586-3

  • Online ISBN: 978-3-642-16587-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics