UAV downwash dynamic texture features for terrain classification on autonomous navigation

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Citations (Scopus)

Abstract

The information generated by a computer vision system capable of labelling a land surface as water, vegetation, soil or other type, can be used for mapping and decision making. For example, an unmanned aerial vehicle (UAV) can use it to find a suitable landing position or to cooperate with other robots to navigate across an unknown region. Previous works on terrain classification from RGB images taken onboard of UAVs shown that only static pixel-based features were tested with a considerable classification error. This paper proposes a robust and efficient computer vision algorithm capable of classifying the terrain from RGB images with improved accuracy. The algorithm complement the static image features with dynamic texture patterns produced by UAVs rotors downwash effect (visible at lower altitudes) and machine learning methods to classify the underlying terrain. The system is validated using videos acquired onboard of a UAV.

Original languageEnglish
Title of host publicationProceedings of the 2018 Federated Conference on Computer Science and Information Systems, FedCSIS 2018
EditorsMaria Ganzha, Leszek Maciaszek, Leszek Maciaszek, Marcin Paprzycki
Place of PublicationNew York
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1079-1083
Number of pages5
ISBN (Electronic)978-8-3949-4195-6
DOIs
Publication statusPublished - 26 Oct 2018
Event2018 Federated Conference on Computer Science and Information Systems, FedCSIS 2018 - Poznan, Poland
Duration: 9 Sep 201812 Sep 2018

Publication series

NameAnnals of Computer Science and Information Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
Volume15
ISSN (Print)2325-0348

Conference

Conference2018 Federated Conference on Computer Science and Information Systems, FedCSIS 2018
Country/TerritoryPoland
CityPoznan
Period9/09/1812/09/18

Keywords

  • Image processing
  • Machine Learning
  • Terrain Classification
  • Texture
  • UAV

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