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A Self-adaptive Approach for Autonomous UAV Navigation via Computer Vision

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Computational Science and Its Applications – ICCSA 2018 (ICCSA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10961))

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Abstract

In autonomous Unmanned Aerial Vehicles (UAVs), the vehicle should be able to manage itself without the control of a human. In these cases, it is crucial to have a safe and accurate method for estimating the position of the vehicle. Although GPS is commonly employed in this task, it is susceptible to failures by different means, such as when a GPS signal is blocked by the environment or by malicious attacks. Aiming to fill this gap, new alternative methodologies are arising such as the ones based on computer vision. This work aims to contribute to the process of autonomous navigation of UAVs using computer vision. Thus, it is presented a self-adaptive approach for position estimation able to change its own configuration for increasing its performance. Results show that an Artificial Neural Network (ANN) presented the best performance as an edge detector for pictures with buildings or roads and the Canny extractor was better at smooth surfaces. Moreover, our self-adaptive approach as a whole shows gain up to \(15\%\) if compared with non-adaptive methodologies.

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Acknowledgments

The authors would like to thank José Renato Garcia Braga for its collaboration and discussions during the development of this work and to Department of Computer and Information Science (IDA) of Linköpings Universitet for providing the images used in this work. Gabriel Fornari would like to acknowledge the scholarship provided by CNPq under the process number \(140694/2016-1\). This work is partially supported by the Swedish Research Council (VR) Linnaeus Center CADICS, ELLIIT, and the Swedish Foundation for Strategic Research (CUAS Project, SymbiKCloud Project).

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Authors and Affiliations

  1. Instituto Nacional de Pesquisas Espaciais, Avenida dos Astronautas, 1758, Jardim da Granja, São José dos Campos, SP, 12227-010, Brazil

    Gabriel Fornari & Valdivino Alexandre de Santiago Júnior

  2. Instituto de Estudos Avançados, Trevo Coronel Aviador José Alberto Albano do Amarante, 1, Putim, São José dos Campos, SP, 12228-001, Brazil

    Elcio Hideiti Shiguemori

Authors
  1. Gabriel Fornari
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  2. Valdivino Alexandre de Santiago Júnior
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  3. Elcio Hideiti Shiguemori
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Correspondence to Gabriel Fornari .

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Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  5. Polytechnic University of Bari, Bari, Italy

    Carmelo M. Torre

  6. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  7. Monash University, Clayton, Victoria, Australia

    David Taniar

  8. Kyushu Sangyo University, Fukuoka shi, Fukuoka, Japan

    Bernady O. Apduhan

  9. Politecnico di Bari, Bari, Italy

    Eufemia Tarantino

  10. Myongji University, Yongin, Korea (Republic of)

    Yeonseung Ryu

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Fornari, G., de Santiago Júnior, V.A., Shiguemori, E.H. (2018). A Self-adaptive Approach for Autonomous UAV Navigation via Computer Vision. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2018. ICCSA 2018. Lecture Notes in Computer Science(), vol 10961. Springer, Cham. https://doi.org/10.1007/978-3-319-95165-2_19

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  • DOI: https://doi.org/10.1007/978-3-319-95165-2_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-95164-5

  • Online ISBN: 978-3-319-95165-2

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