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A Versatile Visual Navigation System for Autonomous Vehicles

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Modelling and Simulation for Autonomous Systems (MESAS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11472))

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Abstract

We present a universal visual navigation method which allows a vehicle to autonomously repeat paths previously taught by a human operator. The method is computationally efficient and does not require camera calibration. It can learn and autonomously traverse arbitrarily shaped paths and is robust to appearance changes induced by varying outdoor illumination and naturally-occurring environment changes. The method does not perform explicit position estimation in the 2d/3d space, but it relies on a novel mathematical theorem, which allows fusing exteroceptive and interoceptive sensory data in a way that ensures navigation accuracy and reliability. The experiments performed indicate that the proposed navigation method can accurately guide different autonomous vehicles along the desired path. The presented system, which was already deployed in patrolling scenarios, is provided as open source at www.github.com/gestom/stroll_bearnav.

The work has been supported by the Czech Science Foundation project 17-27006Y and by the Segurancas roboTicos coOPerativos (STOP) research project (CENTRO-01-0247-FEDER-017562), co-funded by the Agencia Nacional de Inovacao within the Portugal2020 programme.

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Notes

  1. 1.

    http://stop.ingeniarius.pt.

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Acknowledgments

We thank the VOP.cz for sharing their the data and the TAROS vehicle. We would like to thank also Milan Kroulík and Jakub Lev from the Czech University of Life Sciences Prague for their positive attitude and their help to perform experiments with the John Deere tractor.

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Authors and Affiliations

  1. Artificial Intelligence Center, Czech Technical University, Prague, Czech Republic

    Filip Majer, Lucie Halodová, Tomáš Vintr & Tomáš Krajník

  2. Czech University of Life Sciences Prague, Prague, Czech Republic

    Martin Dlouhý

  3. VOP CZ, Šenov u Nového Jičína, Czech Republic

    Lukáš Merenda

  4. Lincoln Center for Autonomous Systems, University of Lincoln, Lincoln, UK

    Jaime Pulido Fentanes

  5. Ingeniarius, Ltd., Coimbra, Portugal

    David Portugal & Micael Couceiro

Authors
  1. Filip Majer
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  2. Lucie Halodová
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  9. Tomáš Krajník
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Correspondence to Tomáš Krajník .

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Editors and Affiliations

  1. NATO Modelling and Simulation Centre of Excellence, Rome, Italy

    Jan Mazal

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Majer, F. et al. (2019). A Versatile Visual Navigation System for Autonomous Vehicles. In: Mazal, J. (eds) Modelling and Simulation for Autonomous Systems. MESAS 2018. Lecture Notes in Computer Science(), vol 11472. Springer, Cham. https://doi.org/10.1007/978-3-030-14984-0_8

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  • DOI: https://doi.org/10.1007/978-3-030-14984-0_8

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