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Localization Fusion for Aerial Vehicles in Partially GNSS Denied Environments

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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))

Abstract

In this paper, we report on early results of the experimental deployment of localization techniques for a multi-rotor Micro Aerial Vehicle (MAV). In particular, we focus on deployment scenarios where the Global Navigation Satellite System (GNSS) does not provide a reliable signal, and thus it is not desirable to rely solely on the GNSS. Therefore, we consider recent advancements in the visual localization, and we employ an onboard RGB-D camera to develop a robust and reliable solution for the MAV localization in partially GNSS denied operational environments. We consider a localization method based on Kalman filter for data fusion of the vision-based localization with the signal from the GNSS. Based on the reported experimental results, the proposed solution supports the localization of the MAV for the temporarily unavailable GNSS, but also improves the position estimation provided by the incremental vision-based localization system while it can run using onboard computational resources of the small vehicle.

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Acknowledgement

This work has been supported by the Technology Agency of the Czech Republic (TAČR) under research Project No. TH03010362.

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Authors and Affiliations

  1. Faculty of Electrical Engineering, Czech Technical University in Prague, Technicka 2, 166 27, Prague, Czech Republic

    Jan Bayer & Jan Faigl

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  1. Jan Bayer
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  2. Jan Faigl
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Correspondence to Jan Bayer .

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Editors and Affiliations

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

    Jan Mazal

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Bayer, J., Faigl, J. (2019). Localization Fusion for Aerial Vehicles in Partially GNSS Denied Environments. 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_20

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  • DOI: https://doi.org/10.1007/978-3-030-14984-0_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-14983-3

  • Online ISBN: 978-3-030-14984-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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