Abstract
Gradient descent in image distances can lead a navigating agent to the goal location, but in environments with an anisotropic distribution of landmarks, gradient home vectors deviate from the true home direction. These deviations can be reduced by applying Newton’s method to matched-filter descent in image distances (MFDID). Based on several image databases we demonstrate that the home vectors produced by the Newton-based MFDID method are usually closer to the true home direction than those obtained from the original MFDID method. The greater accuracy of Newton-MFDID home vectors in the vicinity of the goal location would allow a navigating agent to approach the goal on straighter trajectories, improve the results of triangulation procedures, and enhance a robot’s ability to detect its arrival at a goal.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Binding, D., & Labrosse, F. (2006). Visual local navigation using warped panoramic images. In Proceedings of towards autonomous robotic systems. University of Surrey, Guildford, UK.
Chong, E. K. P., & Żak, S. H. (2001). An introduction to optimization (2nd ed.). New York: Wiley.
Efron, B., & Tibshirani, R. J. (1998). An introduction to the bootstrap. New York: Chapman & Hall/CRC.
Franz, M. O., & Mallot, H. A. (2000). Biomimetic robot navigation. Robotics and Autonomous Systems, special issue: Biomimetic Robots, 30(1–2), 133–153.
Franz, M. O., Schölkopf, B., Mallot, H. A., & Bülthoff, H. H. (1998). Learning view graphs for robot navigation. Autonomous Robots, 5(1), 111–125.
Hafner, V. V. (2000). Cognitive maps for navigation in open environments. In Proceedings 6th international conference on intelligent autonomous systems (IAS-6) (pp. 801–808), Venice. Amsterdam: IOS.
Hübner, W. (2005). From homing behavior to cognitive mapping: integration of egocentric pose relations and allocentric landmark information in a graph model. PhD thesis, Fachbereich 3 (Mathematik & Informatik), Universität Bremen.
Hübner, W., & Mallot, H. A. (2002). Integration of metric place relations in a landmark graph. In J. R. Dorronsoro (Ed.), Lecture notes in computer science : Vol. 2415. Proceedings of the international conference on artificial neural networks (ICANN) (pp. 825–830). Berlin: Springer.
Möller, R., & Vardy, A. (2006). Local visual homing by matched-filter descent in image distances. Biological Cybernetics, 95(5), 413–430.
Ruderman, D. L., & Bialek, W. (1994). Statistics of natural images: scaling in the woods. Physical Review Letters, 73(6), 814–817.
van der Schaaf, A., & van Hateren, J. H. (1996). Modelling the power spectra of natural images: statistics and information. Vision Research, 36(17), 2759–2770.
Vardy, A. (2006). Long-range visual homing. In Proceedings of IEEE international conference on robotics and biomimetics, ROBIO’06 (pp. 220–226).
Vardy, A., & Möller, R. (2005). Biologically plausible visual homing methods based on optical flow techniques. Connection Science, 17(1–2), 47–89.
Zampoglou, M., Szenher, M., & Webb, B. (2006). Adaptation of controllers for image-based homing. Adaptive Behavior, 14(4), 381–399.
Zeil, J., Hoffmann, M. I., & Chahl, J. S. (2003). Catchment areas of panoramic images in outdoor scenes. Journal of the Optical Society of America A, 20(3), 450–469.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Möller, R., Vardy, A., Kreft, S. et al. Visual homing in environments with anisotropic landmark distribution. Auton Robot 23, 231–245 (2007). https://doi.org/10.1007/s10514-007-9043-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10514-007-9043-x