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Combining Invariant Features and the ALV Homing Method for Autonomous Robot Navigation Based on Panoramas

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Abstract

Biologically inspired homing methods, such as the Average Landmark Vector, are an interesting solution for local navigation due to its simplicity. However, usually they require a modification of the environment by placing artificial landmarks in order to work reliably. In this paper we combine the Average Landmark Vector with invariant feature points automatically detected in panoramic images to overcome this limitation. The proposed approach has been evaluated first in simulation and, as promising results are found, also in two data sets of panoramas from real world environments.

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Authors and Affiliations

  1. Artificial Intelligence Research Institute, IIIA - Spanish National Research Council, CSIC, Campus UAB, 08193, Bellaterra, Spain

    Arnau Ramisa, Alex Goldhoorn & Ramon Lopez de Mantaras

  2. Computer Vision Center, CVC - Universitat Autonoma de Barcelona, UAB, 08193, Bellaterra, Spain

    David Aldavert & Ricardo Toledo

Authors
  1. Arnau Ramisa
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  2. Alex Goldhoorn
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  3. David Aldavert
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  4. Ricardo Toledo
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  5. Ramon Lopez de Mantaras
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Corresponding author

Correspondence to Arnau Ramisa.

Additional information

This work was partially supported by the FI grant from the Generalitat de Catalunya, the European Social Fund, the MID-CBR project grant TIN2006-15140- C03-01 and FEDER funds, the grant 2005-SGR-00093, the MIPRCV Consolider Imagennio 2010 and the Marco Polo fund from the University of Groningen.

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Ramisa, A., Goldhoorn, A., Aldavert, D. et al. Combining Invariant Features and the ALV Homing Method for Autonomous Robot Navigation Based on Panoramas. J Intell Robot Syst 64, 625–649 (2011). https://doi.org/10.1007/s10846-011-9552-x

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  • DOI: https://doi.org/10.1007/s10846-011-9552-x

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