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Intelligent Autonomous Systems 13 pp 831–842Cite as

Localization of Unmanned Aerial Vehicles Using Terrain Classification from Aerial Images

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 302))

Abstract

In this paper we investigate the benefit of terrain classification for self-localization of a flying robot. The key idea is to use aerial images, which are already available from online databases such as GoogleMaps™, as reference map and to match images taken with a downward looking camera with this map. Using different terrain classes as features, we can make sure that our method is invariant to lighting/weather changes as well as seasonal variations or minor changes in the environment. A particle filter is used to register the query image with parts of the map. The proposed method has shown to work on image data from both simulated and real flights.

Andreas Masselli and Richard Hanten are with the Chair of Cognitive Systems, headed by Prof. Andreas Zell, at the Faculty of Science, University of Tuebingen.

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Acknowledgments

The authors thank Stefan Laible for his contributions and hints regarding terrain classification, Norbert Morgenstern for performing the outdoor flights, and Sebastian Buck for providing ground truth data.

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Authors and Affiliations

  1. University of Tuebingen, 72076, Tuebingen, Germany

    Andreas Masselli, Richard Hanten & Andreas Zell

Authors
  1. Andreas Masselli
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  2. Richard Hanten
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  3. Andreas Zell
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Corresponding author

Correspondence to Andreas Masselli .

Editor information

Editors and Affiliations

  1. Information Engineering, University of Padua, Padua, Italy

    Emanuele Menegatti

  2. Robotics Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA

    Nathan Michael

  3. Fachbereich Informatik, Technische Universität Kaiserslautern Arbeitsgruppe Robotersysteme, Kaiserslautern, Germany

    Karsten Berns

  4. Integrated Information Technology, Aoyama Gakuin University, Tokyo, Japan

    Hiroaki Yamaguchi

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© 2016 Springer International Publishing Switzerland

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Masselli, A., Hanten, R., Zell, A. (2016). Localization of Unmanned Aerial Vehicles Using Terrain Classification from Aerial Images. In: Menegatti, E., Michael, N., Berns, K., Yamaguchi, H. (eds) Intelligent Autonomous Systems 13. Advances in Intelligent Systems and Computing, vol 302. Springer, Cham. https://doi.org/10.1007/978-3-319-08338-4_60

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  • DOI: https://doi.org/10.1007/978-3-319-08338-4_60

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08337-7

  • Online ISBN: 978-3-319-08338-4

  • eBook Packages: EngineeringEngineering (R0)

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