Skip to main content

Design of a Low-Cost Vision System for Laser Profilometry Aiding Smart Vehicles Movement

  • Conference paper
  • First Online:
Intelligent Autonomous Systems 13

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 302))

Abstract

We present a fast and accurate method to derive the pose of a mobile vehicle moving within bounded paths. A triangulation-based vision system made of a laser source, able to generate a line pattern, and a high speed camera is applied on the front side of an autonomous vehicle, namely the Smoov ASRV platform, which is able to store and retrieve pallets in smart warehouses. The presented system extracts the properties of the emitted laser line on the camera plane and transfers these information to the vehicle reference system. Then, the presence of constitutive landmarks along the path, i.e., holes and bends, permit the estimation of other parameters, such as vehicle speed, enabling the exact control of the vehicle. Further validations have returned accuracies lower than 2 and 3.2 % in distance and tilt measurements with respect to the rail border, respectively.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 349.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 449.99
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

Institutional subscriptions

References

  1. J. Davis, T. Edgar, J. Porterc, J. Bernadend, and Michael Sarlie, “Smart manufacturing, manufacturing intelligence and demand-dynamic performance”, Computers & Chemical Engineering, Vol. 47, pp. 145–156, 2012.

    Google Scholar 

  2. D. Bourne, “My Boss the Robot”, Scientific American, Vol. 308, pp. 38–41, 2013.

    Google Scholar 

  3. J. Borenstein, H.R. Everett, L. Feng, and D. Wehe, “Mobile robot positioning-sensors and techniques”, Journal of Robotic Systems, Special Issue on Mobile Robots, Vol. 14, No. 4, pp. 231–249, 1996.

    Google Scholar 

  4. J. Borenstein, H.R. Everett, and L. Feng, “Navigating Mobile Robots: Systems and Techniques.” A. K. Peters, Ltd., Wellesley, MA, 1996.

    Google Scholar 

  5. T. D’Orazio, M Ianigro, E. Stella, F.P. Lovergine and A. Distante, “Mobile robot navigation by multi-sensory integration,” in Proceedings of the IEEE International Conference on Robotics and Automation, Vol. 2, pp. 373–379, 1993.

    Google Scholar 

  6. G.N. DeSouza and A.C. Kak, “Vision for mobile robot navigation: a survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 2, pp. 237–267, Feb 2002.

    Google Scholar 

  7. T. D’Orazio, F.P. Lovergine, M. Ianigro, E. Stella, and A. Distante, “Mobile robot position determination using visual landmarks,” IEEE Transactions on Industrial Electronics, Vol. 41, No. 6, pp. 654–662, 1994.

    Google Scholar 

  8. M. Oskarsson K. Åström, “Accurate and Automatic Surveying of Beacon Positions for a Laser Guided Vehicle”, Progress in Industrial Mathematics, 1998.

    Google Scholar 

  9. G. Alenya, J. Escoda, A.B. Martinez, and C. Torras, “Using Laser and Vision to Locate a Robot in an Industrial Environment: A Practical Experience”, in Proceedings of the IEEE International Conference on Robotics and Automation, pp. 3528–3533, 2005.

    Google Scholar 

  10. On-line available: http://www.goetting-agv.com/components/43600

  11. F. Duchon, M. Dekan, L. Jurisica, A. Vitko, “Some applications of laser rangefinder in mobile robotics”, Journal of Control Engineering and Applied Informatics, Vol. 14, No. 2, pp. 50–57, 2012.

    Google Scholar 

  12. W. Wei, K. Curran, “Indoor robot localisation with active RFID”, International Journal of Robotics and Automation, Vol. 1, No. 3, pp. 137–144, 2012.

    Google Scholar 

  13. On-line available: http://www.smoov-asrv.eu/

  14. On-line available: http://www.coherent.com/

  15. On-line available: http://www.mikrotron.de/

  16. J. Heikkila, “Geometric camera calibration using circular control points” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 10, pp. 1066–1077, 2000.

    Google Scholar 

  17. D.K. Naidu and R.B. Fisher, “A Comparative Analysis of Algorithms for Determining the Peak Position of a Stripe to Sub-pixel Accuracy”, in Proceedings of British Machine Vision Association Conference (BMVC), pp 217–225, 1991.

    Google Scholar 

Download references

Acknowledgments

This work is supported by the ISSIA-CNR Project PI-LOC (P.O. PUGLIA FESR 2007-2013 LINEA 1.2 – AZIONE 1.2.4). The authors thank the industrial partner ICAM S.r.l (Putignano, Italy) for mechanical implementation of the prototype and Dr. Giuseppe Roselli for his contribution to the design of the system.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Cosimo Patruno .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Patruno, C., Marani, R., Nitti, M., D’Orazio, T., Stella, E. (2016). Design of a Low-Cost Vision System for Laser Profilometry Aiding Smart Vehicles Movement. In: Menegatti, E., Michael, N., Berns, K., Yamaguchi, H. (eds) Intelligent Autonomous Systems 13. Advances in Intelligent Systems and Computing, vol 302. Springer, Cham. https://doi.org/10.1007/978-3-319-08338-4_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-08338-4_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08337-7

  • Online ISBN: 978-3-319-08338-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics