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An Adaptive Particle Filter for Indoor Robot Localization

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Ambient Intelligence - Software and Applications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 376))

Abstract

This paper develops an adaptive particle filter for indoor mobile robot localization, in which two different resampling operations are implemented to adjust the number of particles for fast and reliable computation. Since the weight updating is usually much more computationally intensive than the prediction, the first resampling-procedure so-called partial resampling is adopted before the prediction step, which duplicates the large weighted particles while reserves the rest obtaining better estimation accuracy and robustness. The second resampling, adopted before the updating step, decreases the number of particles through particle merging to save updating computation. In addition to speeding up the filter, sample degeneracy and sample impoverishment are counteracted. Simulations on a typical 1D model and for mobile robot localization are presented to demonstrate the validity of our approach.

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Acknowledgments

This is work is sponsored partly by National Natural Science Foundation of China (Grant No. 51075337) and by the project Sociedades Humano-Agente en entornos Cloud Computing (Soha+C) SA213U13.

Author information

Authors and Affiliations

  1. School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, 710072, China

    Hao Lang, Tiancheng Li & Shudong Sun

  2. BISITE Group, Faulty of Science, University of Salamanca, Salamanca, 37008, Spain

    Tiancheng Li & Gabriel Villarrubia

  3. Department of Artificial Intelligence, Technical University of Madrid, 28660, Madrid, Spain

    Javier Bajo

Authors
  1. Hao Lang
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  2. Tiancheng Li
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  3. Gabriel Villarrubia
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  4. Shudong Sun
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  5. Javier Bajo
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Corresponding author

Correspondence to Tiancheng Li .

Editor information

Editors and Affiliations

  1. Computer Science and Engineering Department, Qatar University, College of Engineering, Doha, Qatar

    Amr Mohamed

  2. Departamento de Informatica, ALGORITMI Centre, University of Minho, Braga, Portugal

    Paulo Novais

  3. Escola Superior de Tecnologia e Gestäo de Leiria, Instituto Politécnico de Leiria, Leiria, Portugal

    António Pereira

  4. Departamento de Informática y Automática, University of Salamanca, Salamanca, Spain

    Gabriel Villarrubia González

  5. Departamento de Sistemas Informáticos, University of Castilla-La Mancha, Albacete, Spain

    Antonio Fernández-Caballero

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© 2015 Springer International Publishing Switzerland

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Lang, H., Li, T., Villarrubia, G., Sun, S., Bajo, J. (2015). An Adaptive Particle Filter for Indoor Robot Localization. In: Mohamed, A., Novais, P., Pereira, A., Villarrubia González, G., Fernández-Caballero, A. (eds) Ambient Intelligence - Software and Applications. Advances in Intelligent Systems and Computing, vol 376. Springer, Cham. https://doi.org/10.1007/978-3-319-19695-4_5

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  • DOI: https://doi.org/10.1007/978-3-319-19695-4_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19694-7

  • Online ISBN: 978-3-319-19695-4

  • eBook Packages: EngineeringEngineering (R0)

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