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More Robust Features for Adaptive Visual Navigation of UAVs in Mixed Environments

A Novel Localisation Framework

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Abstract

In this paper, we present an autonomous visual navigation system that determines the location of the unmanned aerial vehicle (UAV) in GPS-denied environment by detecting semantic features (roads centrelines, intersections, outlines of forest and river) in aerial imagery and matching them to a pre-built dataset. This work is centred around testing the capability of a road centreline modelling and matching algorithm to localise accurately. Alongside, dynamic feature modelling and minimalistic description to optimise data association are proposed. We test three novel datasets with satellite imagery covering the same rural area with significant seasonal and lighting variation.

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Acknowledgements

The authors would like to thank David G. Williams and Dr. Steven J. Dumble for their advice and contributions to this work.

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Authors and Affiliations

  1. University of Sydney, Sydney, Australia

    Anastasiia Volkova

  2. School of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney, NSW, 2006, Sydney, Australia

    Peter W. Gibbens

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  1. Anastasiia Volkova
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  2. Peter W. Gibbens
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Correspondence to Anastasiia Volkova.

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Volkova, A., Gibbens, P.W. More Robust Features for Adaptive Visual Navigation of UAVs in Mixed Environments. J Intell Robot Syst 90, 171–187 (2018). https://doi.org/10.1007/s10846-017-0650-2

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  • DOI: https://doi.org/10.1007/s10846-017-0650-2

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