Autonomous Robots

, 27:373 | Cite as

Robust vision-based robot localization using combinations of local feature region detectors

  • Arnau Ramisa
  • Adriana Tapus
  • David Aldavert
  • Ricardo Toledo
  • Ramon Lopez de Mantaras
Article

Abstract

This paper presents a vision-based approach for mobile robot localization. The model of the environment is topological. The new approach characterizes a place using a signature. This signature consists of a constellation of descriptors computed over different types of local affine covariant regions extracted from an omnidirectional image acquired rotating a standard camera with a pan-tilt unit. This type of representation permits a reliable and distinctive environment modelling. Our objectives were to validate the proposed method in indoor environments and, also, to find out if the combination of complementary local feature region detectors improves the localization versus using a single region detector. Our experimental results show that if false matches are effectively rejected, the combination of different covariant affine region detectors increases notably the performance of the approach by combining the different strengths of the individual detectors. In order to reduce the localization time, two strategies are evaluated: re-ranking the map nodes using a global similarity measure and using standard perspective view field of 45°.

In order to systematically test topological localization methods, another contribution proposed in this work is a novel method to see the degradation in localization performance as the robot moves away from the point where the original signature was acquired. This allows to know the robustness of the proposed signature. In order for this to be effective, it must be done in several, variated, environments that test all the possible situations in which the robot may have to perform localization.

Keywords

Topological localization Vision based localization Panoramic vision Affine covariant region detectors 

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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Arnau Ramisa
    • 1
  • Adriana Tapus
    • 2
  • David Aldavert
    • 3
  • Ricardo Toledo
    • 3
  • Ramon Lopez de Mantaras
    • 1
  1. 1.Artificial Intelligence Research Institute (IIIA, CSIC)BarcelonaSpain
  2. 2.University of Southern CaliforniaLos AngelesUSA
  3. 3.Computer Vision Center (CVC)BarcelonaSpain
  4. 4.ENSTA-ParisTechParisFrance

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