Skip to main content

Integration of Metric Place Relations in a Landmark Graph

  • Conference paper
  • First Online:
Artificial Neural Networks — ICANN 2002 (ICANN 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2415))

Included in the following conference series:

Abstract

This paper describes a graph embedding procedure which extends the topologic information of a landmark graph with position estimates. The graph is used as an environment map for an autonomous agent, where the graph nodes contain information about places in two different ways: a panoramic image containing the landmark configuration and the estimated recording position. Calculation of the graph embedding is done with a modified “multidimensional scaling” algorithm, which makes use of distances and angles between nodes. It will be shown that especially graph circuits are responsible for preventing the path integration error from unbounded growth. Furthermore a heuristic for the MDS-algorithm is described, which makes this scheme applicable to the exploration of larger environments. The algorithm is tested with an agent building a map of a virtual environment.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /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
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

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. Benhamou S., Séguinot V.: How to find one’s way in the labyrinth of path integration models. J. theor. Biol. 174 (1995) 463–466

    Article  Google Scholar 

  2. Cartwright B.A., Collet T.S.: Landmark learning in bees. Journal of Computational Physiology A 151 (1983) 521–543

    Article  Google Scholar 

  3. Franz M.O., Schölkopf B., Mallot H.A., Bülthoff H.: Where did I take that snapshot? Scenebased homing by image matching. Biol. Cybernetics 79(3) (1998) 191–202

    Article  MATH  Google Scholar 

  4. Kuipers B.: The spatial semantic hierarchy. Artifical Intelligence 119 (2000) 191–233

    Article  MATH  MathSciNet  Google Scholar 

  5. Mardia K.V., Kent J.T., Bibby J.M.: Multivariate Analysis. Academic Press, Inc., (1982) 413–415

    Google Scholar 

  6. Lu F., Milios, E.: Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans. Journal of Intelligent and Robotic Systems 18 (1997) 249–275

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2002 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Hübner, W., Mallot, H.A. (2002). Integration of Metric Place Relations in a Landmark Graph. In: Dorronsoro, J.R. (eds) Artificial Neural Networks — ICANN 2002. ICANN 2002. Lecture Notes in Computer Science, vol 2415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46084-5_134

Download citation

  • DOI: https://doi.org/10.1007/3-540-46084-5_134

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44074-1

  • Online ISBN: 978-3-540-46084-8

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics