Skip to main content
SpringerLink
Log in

An Orientation Invariant Visual Homing Algorithm

  • Published:
Journal of Intelligent & Robotic Systems Aims and scope Submit manuscript

Abstract

Visual homing is the ability of an agent to return to a goal position by comparing the currently viewed image with an image captured at the goal, known as the snapshot image. In this paper we present additional mathematical justification and experimental results for the visual homing algorithm first presented in Churchill and Vardy (2008). This algorithm, known as Homing in Scale Space, is far less constrained than existing methods in that it can infer the direction of translation without any estimation of the direction of rotation. Thus, it does not require the current and snapshot images to be captured from the same orientation (a limitation of some existing methods). The algorithm is novel in its use of the scale change of SIFT features as an indication of the change in the feature’s distance from the robot. We present results on a variety of image databases and on live robot trials.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Churchill, D., Vardy, A.: Homing in scale space. In: IEEE/RSJ International Conference on Robots and Systems (IROS), pp. 1307–1312 (2008)

  2. Cartwright, B., Collett, T.: Landmark learning in bees. J. Comp. Physiol., A 151, 521–543 (1983)

    Article  Google Scholar 

  3. Cartwright, B., Collett, T.: Landmark maps for honeybees. Biol. Cybern. 57, 85–93 (1987)

    Article  Google Scholar 

  4. Anderson, A.: A model for landmark learning in the honey-bee. J. Comp. Physiol., A 114, 335–355 (1977)

    Article  Google Scholar 

  5. Wehner, R., Michel, B., Antonsen, P.: Visual navigation in insects: coupling of egocentric and geocentric information. J. Exp. Biol. 199, 129–140 (1996)

    Google Scholar 

  6. Graham, P., Durier, V., Collett, T.: The binding and recall of snapshot memories in wood ants (Formica rufa L.). J. Exp. Biol. 207, 393–398 (2003)

    Article  Google Scholar 

  7. Morris, R.: Spatial localization does not require the presence of local cues. Learn. Motiv. 12, 239–260 (1981)

    Article  Google Scholar 

  8. Gillner, S., Weiss, A., Mallot, H.: Visual homing in the absecne of feature-based landmark information. Cognition 109, 105–122 (2008)

    Article  Google Scholar 

  9. Collett, T., Collett, M.: Memory use in insect visual navigation. Nat. Rev. Neurosci. 3, 542–552 (2002)

    Article  Google Scholar 

  10. Kuipers, B., Byun, Y.-T.: A robot exploration and mapping strategy based on a semantic hierarchy of spatial representations. Robot. Auton. Syst. 8, 47–63 (1991)

    Article  Google Scholar 

  11. Hong, J., Tan, X., Pinette, B., Weiss, R., Riseman, E.: Image-based homing. In: IEEE ICRA, pp. 620–625 (1991)

  12. Argyros, A., Bekris, C., Orphanoudakis, S., Kavraki, L.: Robot homing by exploiting panoramic vision. Auton. Robots 19(1), 7–25 (2005)

    Article  Google Scholar 

  13. Vardy, A.: Long-range visual homing. In: Proceedings of the IEEE International Conference on Robotics and Biomimetics. IEEE Xplore (2006)

  14. Franz, M., Schölkopf, B., Mallot, H., Bülthoff, H.: Learning view graphs for robot navigation. Auton. Robots 5, 111–125 (1998)

    Article  Google Scholar 

  15. Hübner, W., Mallot, H.: Metric embedding of view-graphs: a vision and odometry-based approach to cognitive mapping. Auton. Robots 23, 183–196 (2007)

    Article  Google Scholar 

  16. Goedemé, T., Nuttin, M., Tuytelaars, T., Van Gool, L.: Omnidirectional vision based topological navigation. Int. J. Comput. Vis. 74(3), 219–236 (2007)

    Article  Google Scholar 

  17. Filliat, D.: Interactive learning of visual topological navigation. In: IEEE/RSJ International Conference on Robots and Systems (IROS) (2008)

  18. Dai, D., Lawton, D.: Range-free qualitative navigation. In: IEEE ICRA (1993)

  19. Franz, M., Schölkopf, B., Mallot, H., Bülthoff, H.: Where did I take that snapshot? Scene-based homing by image matching. Biol. Cybern. 79, 191–202 (1998)

    Article  MATH  Google Scholar 

  20. Möller, R., Vardy, A.: Local visual homing by matched-filter descent in image distances. Biol. Cybern. 95, 413–430 (2006)

    Article  Google Scholar 

  21. Zeil, J., Hofmann, M., Chahl, J.: Catchment areas of panoramic snapshots in outdoor scenes. J. Opt. Soc. Am. A 20(3), 450–469 (2003)

    Article  Google Scholar 

  22. Vardy, A., Möller, R.: Biologically plausible visual homing methods based on optical flow techniques. Connect. Sci. 17(1/2), 47–90 (2005)

    Article  Google Scholar 

  23. Zampoglou, M., Szenher, M., Webb, B.: Adaptation of controllers for image-based homing. Adapt. Behav. 14, 245–252 (2006)

    Article  Google Scholar 

  24. Möller, R.: Local visual homing by warping of two-dimensional images. Robot. Auton. Syst. 57(1), 87–101 (2009)

    Article  Google Scholar 

  25. Möller, R., Krzykawski, M., Gerstmayr, L.: Three 2d-warping schemes for visual robot navigation. Auton. Robots 29(3), 253–291 (2010)

    Article  Google Scholar 

  26. Burke, A., Vardy, A.: Visual compass methods for robot navigation. In: Proceedings of the Newfoundland Conference on Electrical and Computer Engineering (2006)

  27. Vardy, A.: A simple visual compass with learned pixel weights. In: Proceedings of the Canadian Conference on Electrical and Computer Engineering. IEEE Xplore (2008)

  28. Rizzi, A., Duina, D., Inelli, S., Cassinis, R.: Unsupervised matching of visual landmarks for robotic homing using Fourier-Mellin transform. In: Intelligent Autonomous Systems vol. 6, pp. 455–462 (2000)

  29. Vardy, A., Oppacher, F.: Low-level visual homing. In: Banzhaf, W., Christaller, T., Dittrich, P., Kim, J.T., Ziegler, J. (eds.) Advances in Artificial Life—Proceedings of the 7th European Conference on Artificial Life (ECAL). Lecture Notes in Artificial Intelligence, vol. 2801, pp. 875–884. Springer (2003)

  30. Weber, K., Venkatesh, S., Srinivasan, M.: Insect-inspired robotic homing. Adapt. Behav. 7, 65–97 (1999)

    Article  Google Scholar 

  31. Lambrinos, D., Möller, R., Labhart, T., Pfeifer, R., Wehner, R.: A mobile robot employing insect strategies for navigation. Robot. Auton. Syst., Special Issue: Biomimetic Robots 30, 39–64 (2000)

    Article  Google Scholar 

  32. Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  Google Scholar 

  33. Se, S., Lowe, D., Little, J.: Mobile robot localization and mapping with uncertainty using scale-invariant visual landmarks. Int. J. Rob. Res. 21(8), 735–758 (2001)

    Article  Google Scholar 

  34. Briggs, A., Li, Y., Scharstein, D., Wilder, M.: Robot navigation using 1d panoramic images. In: IEEE ICRA, pp. 2679–2685 (2006)

  35. Pons, J.S., Hübner, W., Dahmen, J., Mallot, H.: Vision-based robot homing in dynamic environments. In: Schilling, K. (ed.) 13th IASTED International Conference on Robotics and Applications, pp. 293–298 (2007)

  36. Vardy, A.: Using feature scale change for robot localization along a route. In: IEEE/RSJ International Conference on Robots and Systems (IROS) (2010)

  37. Röfer, T.: Controlling a wheelchair with image-based homing. In: Proceedings of AISB Workshop on Spatial Reasoning in Mobile Robots and Animals. Manchester, UK (1997)

  38. Möller, R., Vardy, A., Kreft, S., Ruwisch, S.: Visual homing in environments with anisotropic landmark distribution. Auton. Robots 23, 231–245 (2007)

    Article  Google Scholar 

  39. Churchill, D.: Homing in scale space. Master’s thesis, Memorial University of Newfoundland (2009)

  40. Royston, P.: An extension of shapiro and wilk’s w test for normality to large samples. In: Applied Statistics, pp. 115–124 (1982)

  41. Royston, P.: Algorithm as 181: the w test for normality. In: Applied Statistics, pp. 176–180 (1982)

  42. Gibbons, J., Chakraborti, S.: Nonparametric Statistical Inference. Marcel Dekker, New York (1992)

    MATH  Google Scholar 

  43. Kitchens, L.: Basic Statistics and Data Analysis. Duxbury (2003)

  44. Lehmann, E.L.: Nonparametrics: Statistical Methods Based on Ranks. Holden and Day, San Francisco (1975)

    MATH  Google Scholar 

  45. Franz, M., Mallot, H.: Biomimetic robot navigation. Robot. Auton. Syst., Special Issue: Biomimetic Robots 30, 133–153 (2000)

    Article  Google Scholar 

  46. Ferdaus, S., Vardy, A., Mann, G., Gosine, R.: Comparing global measures of image similarity for use in topological localization of mobile robots. In: Proceedings of the Canadian Conference on Electrical and Computer Engineering. IEEE Xplore (2008)

  47. Bay, H., Ess, A., Tuytelaars, T., Gool, L.V.: SURF: speeded up robust features. Comput. Vis. Image Underst. 110(3), 346–359 (2008)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Alberta, Edmonton, Canada

    David Churchill

  2. Department of Computer Science, Memorial University of Newfoundland, St. John’s, Canada

    Andrew Vardy

Authors
  1. David Churchill
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andrew Vardy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Andrew Vardy.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Churchill, D., Vardy, A. An Orientation Invariant Visual Homing Algorithm. J Intell Robot Syst 71, 3–29 (2013). https://doi.org/10.1007/s10846-012-9730-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10846-012-9730-5

Keywords

Search

Navigation