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Bio-Inspired Scan Matching for Efficient Simultaneous Localization and Mapping

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Computational Science and Its Applications – ICCSA 2021 (ICCSA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12952))

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Abstract

Simultaneous Localization And Mapping (SLAM) is a fundamental problem in robotic systems. In this work, we apply a derivative free bio-inspired techniques to the scan-matching step, which is a the main step in the SLAM problem based on the exploitation of swarm intelligence. For this purpose, we have explore the swarm intelligence optimization meta-heuristics based on the firefly behavior. Aiming at reducing further the translational and rotational scan alignment errors, the proposed scan matching proceeds in two main steps, namely scan-to-scan and scan-to-map matching. The proposed firefly-based implementation of the SLAM provides a good trade-off regarding accuracy and execution efficiency.

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Acknowledgments

This study is financed in part by the Coordenação de Aperfeicoiamento de Pessoal de Nível Superior, Brasil (CAPES), Finance Code 001.

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Authors and Affiliations

  1. Department of Electronics Engineering and Telecommunications, State University of Rio de Janeiro, Rio de Janeiro, Brazil

    Nadia Nedjah & Pedro Jorge Albuquerque de Oliveira

  2. Department of Systems Engineering and Computation, State University of Rio de Janeiro, Rio de Janeiro, Brazil

    Luiza de Macedo Mourelle

Authors
  1. Nadia Nedjah
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  2. Luiza de Macedo Mourelle
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  3. Pedro Jorge Albuquerque de Oliveira
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Corresponding author

Correspondence to Nadia Nedjah .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Cagliari, Cagliari, Italy

    Ivan Blečić

  6. Monash University, Clayton, VIC, Australia

    David Taniar

  7. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  8. University of Minho, Braga, Portugal

    Ana Maria A. C. Rocha

  9. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

  10. Polytechnic University of Bari, Bari, Italy

    Carmelo Maria Torre

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Nedjah, N., Mourelle, L.d.M., de Oliveira, P.J.A. (2021). Bio-Inspired Scan Matching for Efficient Simultaneous Localization and Mapping. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2021. ICCSA 2021. Lecture Notes in Computer Science(), vol 12952. Springer, Cham. https://doi.org/10.1007/978-3-030-86973-1_25

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  • DOI: https://doi.org/10.1007/978-3-030-86973-1_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86972-4

  • Online ISBN: 978-3-030-86973-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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