Skip to main content
SpringerLink
Log in

Vision-Based Robot Localization Using Sporadic Features

  • Conference paper
  • First Online:
Robot Vision (RobVis 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1998))

Included in the following conference series:

Abstract

Knowing its position in an environment is an essential capability for any useful mobile robot. Monte-Carlo Localization (MCL) has become a popular framework for solving the self-localization problem in mobile robots. The known methods exploit sensor data obtained from laser range finders or sonar rings to estimate robot positions and are quite reliable and robust against noise. An open question is whether comparable localization performance can be achieved using only camera images, especially if the camera images are used both for localization and object recognition. In this paper, we discuss the problems arising from these characteristics and showex perimentally that MCL nevertheless works very well under these conditions.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. F. Dellaert, W. Burgard, D. Fox, S. Thrun: Using the condensation algorithm for robust, vision-based mobile robot localization. In: Proceed. IEEE Conf. on Computer Vision and Pattern Recognition, (1999). 36

    Google Scholar 

  2. S. Enderle: The sparrow99 robot. Technical report, University of Ulm, (1999). 39

    Google Scholar 

  3. S. Enderle, M. Ritter, D. Fox, S. Sablatnög, G. Kraetzschmar, G. Palm: Soccerrobot locatization using sporadic visual features. Proceed. of the IAS-6 Internat. Conf. on Intelligent Autonomous Systems, (2000). 37

    Google Scholar 

  4. D. Fox: Markov localization: a probabilistic framework for mobile robot localization and navigation. PhD thesis, University of Bonn, Bonn, Germany, (December 1998). 35

    Google Scholar 

  5. D. Fox, W. Burgard, F. Dellaert, S. Thrun: Monte carlo localization: efficient position estimation for mobile robots. In: Proceed. of the Conf. on Artificial Intelligence, AAAI, (1999). 35

    Google Scholar 

  6. J.-S. Gutmann, W. Burgard, D. Fox, K. Konolige: An experimental comparison of localization methods. In: Proceed. of the Internat. Conf. on Intelligent Robots and Systems, IROS’98, Victoria, Canada, (October 1998). 35

    Google Scholar 

  7. H. Kitano, M. Asada, Y. Kuniyoshi, I. Noda, E. Osawa, H. Matsubara: Robocup a challenge problem for AI. AI magazine, 18 (1997) 73–85. 35

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dept. of Neural Information Processing, University of Ulm, D-89069, Ulm, Germany

    Stefan Enderle, Heiko Folkerts, Marcus Ritter, Stefan Sablatnög, Gerhard Kraetzschmar & Günther Palm

Authors
  1. Stefan Enderle
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Heiko Folkerts
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Marcus Ritter
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Stefan Sablatnög
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Gerhard Kraetzschmar
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Günther Palm
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Center for Image Technology and Robotics (CITR Tamaki), The University of Auckland, Tamaki Campus, Building 731, 1005, Auckland, New Zealand

    Reinhard Klette

  2. Department of Computer Science, The Hebrew University of Jerusalem, Givat Ram, Ross Building, 91904, Jerusalem, Israel

    Shmuel Peleg

  3. Institut für Informatik, Universitäet Kiel, Preusserstr.1-9, 24105, Kiel, Germany

    Gerald Sommer

Rights and permissions

Reprints and permissions

Copyright information

© 2001 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Enderle, S., Folkerts, H., Ritter, M., Sablatnög, S., Kraetzschmar, G., Palm, G. (2001). Vision-Based Robot Localization Using Sporadic Features. In: Klette, R., Peleg, S., Sommer, G. (eds) Robot Vision. RobVis 2001. Lecture Notes in Computer Science, vol 1998. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44690-7_5

Download citation

  • DOI: https://doi.org/10.1007/3-540-44690-7_5

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41694-4

  • Online ISBN: 978-3-540-44690-3

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation