More Robust Features for Adaptive Visual Navigation of UAVs in Mixed Environments
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-bu...
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| Veröffentlicht in: | Journal of intelligent & robotic systems 2018-05, Vol.90 (1-2), p.171-187 |
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| container_title | Journal of intelligent & robotic systems |
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| creator | Volkova, Anastasiia Gibbens, Peter W |
| description | 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. |
| doi_str_mv | 10.1007/s10846-017-0650-2 |
| format | Article |
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| ispartof | Journal of intelligent & robotic systems, 2018-05, Vol.90 (1-2), p.171-187 |
| issn | 0921-0296 1573-0409 |
| language | eng |
| recordid | cdi_proquest_journals_2015453948 |
| source | Springer Nature - Complete Springer Journals |
| subjects | Autonomous navigation Model matching Modelling Navigation systems Rural areas Satellite imagery Traffic intersections Unmanned aerial vehicles |
| title | More Robust Features for Adaptive Visual Navigation of UAVs in Mixed Environments |
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