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A Versatile Visual Navigation System for Autonomous Vehicles

  • Filip Majer
  • Lucie Halodová
  • Tomáš Vintr
  • Martin Dlouhý
  • Lukáš Merenda
  • Jaime Pulido Fentanes
  • David Portugal
  • Micael Couceiro
  • Tomáš KrajníkEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11472)

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.

Notes

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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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Artificial Intelligence CenterCzech Technical UniversityPragueCzech Republic
  2. 2.Czech University of Life Sciences PraguePragueCzech Republic
  3. 3.VOP CZŠenov u Nového JičínaCzech Republic
  4. 4.Lincoln Center for Autonomous SystemsUniversity of LincolnLincolnUK
  5. 5.Ingeniarius, Ltd.CoimbraPortugal

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