Bearing similarity measures for self-organizing feature maps

  • Authors:

  • Narongdech Keeratipranon;Frederic Maire

  • Affiliations:

  • Faculty of Information Technology, Queensland University of Technology, Brisbane, Australia;Faculty of Information Technology, Queensland University of Technology, Brisbane, Australia

  • Venue:
  • IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
  • Year:
  • 2005

Quantified Score

Hi-index 0.00

Visualization

Abstract

The neural representation of space in rats has inspired many navigation systems for robots. In particular, Self-Organizing (Feature) Maps (SOM) are often used to give a sense of location to robots by mapping sensor information to a low-dimensional grid. For example, a robot equipped with a panoramic camera can build a 2D SOM from vectors of landmark bearings. If there are four landmarks in the robot’s environment, then the 2D SOM is embedded in a 2D manifold lying in a 4D space. In general, the set of observable sensor vectors form a low-dimensional Riemannian manifold in a high-dimensional space. In a landmark bearing sensor space, the manifold can have a large curvature in some regions (when the robot is near a landmark for example), making the Eulidian distance a very poor approximation of the Riemannian metric. In this paper, we present and compare three methods for measuring the similarity between vectors of landmark bearings. We also discuss a method to equip SOM with a good approximation of the Riemannian metric. Although we illustrate the techniques with a landmark bearing problem, our approach is applicable to other types of data sets.