Title: Application of vision-based particle filter and visual odometry for UAV localization
Authors: Jurevičius, Rokas
Marcinkevičius, Virginijus
Citation: WSCG '2017: short communications proceedings: The 25th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision 2016 in co-operation with EUROGRAPHICS: University of West Bohemia, Plzen, Czech RepublicMay 29 - June 2 2017, p. 67-71.
Issue Date: 2017
Publisher: Václav Skala - UNION Agency
Document type: konferenční příspěvek
URI: wscg.zcu.cz/WSCG2017/!!_CSRN-2702.pdf
ISBN: 978-80-86943-45-9
ISSN: 2464-4617
Keywords: lokalizace filtru částic;GPS-odmítnutá navigace;vizuální odometry;KLD vzorkování;korelační koeficient
Keywords in different language: particle filter localization;GPS-denied navigation;visual odometry;KLD sampling;correlation coefficient
Abstract: Conventional UAV (abbr. Unmanned Air Vehicle) auto-pilot systems uses GPS signal for navigation. While the GPS signal is lost, jammed or the UAV is navigating in GPS-denied environment conventional autopilot systems fail to navigate safely. UAV should estimate it’s own position without the need of external signals. Localization, the process of pose estimation relatively to known environment, may solve the problem of navigation without GPS signal. Downward looking camera on a UAV may be used to solve pose estimation problem in combination with visual odometry and other sensor data. In this paper a vision-based particle filter application is proposed to solve GPS-denied UAV localization. The application uses visual odometry for motion estimation, correlation coefficient for apriori known map image matching with aerial imagery, KLD (abbr. Kueller-Leiblach distance) sampling for particle filtering. Research using data collected during real UAV flight is performed to investigate: UAV heading influence on correlation coefficient values when matching aerial imagery with the map and measure localization accuracy compared to conventional GPS system and state-of-the-art odometry.
Rights: © Václav Skala - UNION Agency
Appears in Collections:WSCG '2017: Short Papers Proceedings

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