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Safe landing zones detection for UAVS using deep regression

From National Research Council Canada

DOIResolve DOI: https://doi.org/10.1109/CRV55824.2022.00035
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Affiliation
  1. National Research Council of Canada. Digital Technologies
FunderSearch for: National Research Council of Canada's Artificial Intelligence
FormatText, Article
Conference2022 19th Conference on Robots and Vision (CRV), May 31 - June 2, 2022, Toronto, ON, Canada
Subjectautomatic UAV navigation; safe landing zones (SLZ); semantic segmentation; deep regression; image segmentation; three-dimensional displays; navigation; urban areas; semantics; vision sensors; predictive models
Abstract
Finding safe landing zones (SLZ) in urban areas and natural scenes is one of the many challenges that must be overcome in automating Unmanned Aerial Vehicles (UAV) navigation. Using passive vision sensors to achieve this objective is a very promising avenue due to their low cost and the potential they provide for performing simultaneous terrain analysis and 3D reconstruction. In this paper, we propose using a deep learning approach on UAV imagery to assess the SLZ. The model is built on a semantic segmentation architecture whereby thematic classes of the terrain are mapped into safety scores for UAV landing. Contrary to past methods, which use hard classification into safe/unsafe landing zones, our approach provides a continuous safety map that is more practical for an emergency landing. Experiments on public datasets have shown promising results.
Publication date
PublisherIEEE
In
  • 2022 19th Conference on Robots and Vision (CRV), 9867062 (29 August 2022): 213–218.
LanguageEnglish
Peer reviewedYes
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Record identifierb594c65b-af7a-4004-bd4a-2a2ac9748554
Record created2022-09-09
Record modified2023-03-16
Date modified: