A System to Navigate a Robot into a Ship Structure | SpringerLink
Skip to main content

A System to Navigate a Robot into a Ship Structure

  • Conference paper
  • First Online:
Computer Vision Systems (ICVS 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2095))

Included in the following conference series:

  • 534 Accesses

Abstract

A prototype system has been built to navigate a walking robot into a ship structure. The robot is equipped with a stereo head for monocular and stereo vision. From the CAD-model of the ship good viewpoints are selected such that the head can look at locations with sufficient features. The edge features for the views are extracted automatically. The pose of the robot is estimated from the features detected by two vision approaches. One approach searches in the full image for junctions and uses the stereo information to extract 3D information. The other method is monocular and tracks 2D edge features. To achieve robust tracking of the features a model-based tracking approach is enhanced with a method of Edge Projected Integration of Cues (EPIC). EPIC uses object knowledge to select the correct features in real-time. The two vision systems are synchronised by sending the images over a fibre channel network. The pose estimation uses both the 2D and 3D features and locates the robot within a few centimetres over the range of ship cells of several metres. Gyros are used to stabilise the head while the robot moves. The system has been developed within the RobVision project and the results of the final demonstration are given.

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

Access this chapter

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 5719
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 7149
Price includes VAT (Japan)
  • 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Alferez, R., Wang, Y.F.: Geometric and Illumination Invariants for Object Recognition, IEEE Transactions on PAMI 21(6), 1999, pp. 505–536.

    Google Scholar 

  2. C. G. Bräutigam: A Model-Free Voting Approach to Cue Integration, Dissertation, KTH Stockholm, 1998.

    Google Scholar 

  3. J.S. Beis, D.G. Lowe, Indexing without Invariants in 3D Object Recognition, Pattern Analysis and Machine Intelligence, Vol.21,No.10, 1999, pp. 1000–1015.

    Article  Google Scholar 

  4. J.R. Beveridge, E.M. Riseman, How Easy is Matching 2D Line Models Using Local Search?, Pattern Analysis and Machine Intelligence, Vol.19,No.19, 1997, pp.564–579.

    Article  Google Scholar 

  5. H.I. Christensen, D. Kragic: Robust Vision for Manipulation and Navigation, in: Markus Vincze (ed.): Proc. Robust Vision for Industrial Applications 1999, 23rd Workshop ÆAGM/AAPR. [Del00] Deliverable of the RobVision project, also available from http://www.robvision.infa.tuwien.ac.at, 2000.

  6. Dickinson, S.J., Wilkes, D., Tsotsos, J.K.: A Computational Model of View Degeneracy, IEEE Transactions on PAMI, vol.21,no.8, 1999, 673–689.

    Google Scholar 

  7. C. Eberst, M. Barth, et.al., Robust Vision-based Object Recognition Integrating Highly Redundant Cues for Indexing and Verification, IEEE ICRA 2000, pp. 3757–3764.

    Google Scholar 

  8. Förstner, W. and Gülch, E., (1987): A Fast Operator for Detection and Precise Location of Distinct Points, Corners and Centers of Circular Features; ISPRS Intercommission Workshop, Interlaken.

    Google Scholar 

  9. Fuerst, S., Dickmanns, E.D.: A Vision Based Navigation System for Autonomous Aircraft; Robotics and Autonomous Systems 28, pp.173–184, 1999.

    Article  Google Scholar 

  10. A. Gasteratos and G. Sandini, On the Accuracy of the Eurohead, LIRA–TR 2/00, July 2000.

    Google Scholar 

  11. Grimson, W.E.L.: Object Recognition by Computer, MIT Press, 1990.

    Google Scholar 

  12. Hager, G. and Toyama, K. (1998): The XVision-System: A Portable Substrate for Real-Time Vision Applications, Computer Vision and Image Understanding, 69(1), 23–37.

    Article  Google Scholar 

  13. Garding, J. and Lindeberg, T. (1994): Direct Estimation of Local Surface Shape in a Fixating Binocular Vision System, in: J. O. Eklundh, Lecture Notes in Comp. Science, 800, 365–376.

    Google Scholar 

  14. D.S. Kim, R. Nevatia, Recognition and localization of generic objects for indoor navigation using functionality, Image and Vision Computing 16(11), Special Issue SI, 1998 Aug 1, 729–743.

    Article  Google Scholar 

  15. Kosaka, A., Nakazawa, G.: Vision-Based Motion Tracking of Rigid Objects Using Prediction of Uncertainties; ICRA, pp.2637–2644, 1995.

    Google Scholar 

  16. D. Kragic, H.I. Christensen, Cue Integration for Manipulation, in [21], pp. 1–16.

    Google Scholar 

  17. P. Krautgartner, M. Vincze: Optimal Image Processing Architecture for Active Vision Systems; Int. Conf. on Vision Systems, Gran Canaria, S. 331–347, January 13–15, 1999.

    Google Scholar 

  18. P. Pirjanian, H.I. Christensen, J.A. Fayman, Application of Voting to fusion of Purposive Modules: An Experimental Investigation, Robotics & Autonomous Sys., Vol.23, 1998, pp. 253–266.

    Article  Google Scholar 

  19. Smith, S. M. and Brady, J. M. (1997): SUSAN-a new approach to low level image processing, Int. Journal of Computer Vision, 23,(1), 45–78.

    Article  Google Scholar 

  20. M. Vincze, M. Ayromlou, W. Kubinger: An Integrating Framework for Robust Real-Time 3D Object Tracking; Int. Conf. on Vision Systems, Gran Canaria, S. 135–150, January 13–15, 1999.

    Google Scholar 

  21. M. Vincze, G.D. Hager, Eds., Robust Vision for Vision-Based Control of Motion, IEEE Press, 2000.

    Google Scholar 

  22. M. Vincze: Robust Tracking of Ellipses at Frame Rate; Pattern Recognition 34(2), 487–498, 2001.

    Article  MATH  Google Scholar 

  23. Wunsch, P., Hirzinger, G.: Real-Time Visual Tracking of 3-D Objects with Dynamic Handling of Occlusion; ICRA, pp.2868–2873, 1997.

    Google Scholar 

  24. A. Zisserman, D. Forsyth, et.al., 3D object recognition using invariance, Artificial Intelligence, Vol.78, 1995, pp. 239–288.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2001 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Vincze, M. et al. (2001). A System to Navigate a Robot into a Ship Structure. In: Schiele, B., Sagerer, G. (eds) Computer Vision Systems. ICVS 2001. Lecture Notes in Computer Science, vol 2095. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48222-9_18

Download citation

  • DOI: https://doi.org/10.1007/3-540-48222-9_18

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42285-3

  • Online ISBN: 978-3-540-48222-2

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics