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A multi-layered approach for site detection in UAS emergency landing scenarios using geometry-based image segmentation

By Luis Mejias and Daniel L. Fitzgerald

Abstract

This paper presents an alternative approach to image segmentation by using the spatial distribution of edge pixels as opposed to pixel intensities. The segmentation is achieved by a multi-layered approach and is intended to find suitable landing areas for an aircraft emergency landing. We combine standard techniques (edge detectors) with novel developed algorithms (line expansion and geometry test) to design an original segmentation algorithm. Our approach removes the dependency on environmental factors that traditionally influence lighting conditions, which in turn have negative impact on pixel-based segmentation techniques. We present test outcomes on realistic visual data collected from an aircraft, reporting on preliminary feedback about the performance of the detection. We demonstrate consistent performances over 97% detection rate

Topics: 080104 Computer Vision, 090104 Aircraft Performance and Flight Control Systems, UAV Forced Landing, UAS, UAV, Vision-Based Forced Landing, CEDM
Publisher: IEEE Control Society
Year: 2013
DOI identifier: 10.1109/ICUAS.2013.6564710
OAI identifier: oai:eprints.qut.edu.au:60550

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Citations

  1. (1986). A computational approach to edge detection,”
  2. (2007). A flight simulation algorithm for a parafoil suspending an air vehicle,”
  3. (1993). A review on image segmentation techniques,”
  4. (2013). A vision-based automatic safe landing-site detection system,” Aerospace and Electronic Systems,
  5. (2010). Airborne systems laboratory for automation research,”
  6. (2010). Automating human thought processes for a uav forced landing,”
  7. (2004). Autonomous landing of an unmanned helicopter based on vision and inbertial sensing.”
  8. (1999). Color image segmentation,”
  9. (1985). Computer vision graphics image processing,” Survey: image segmentation techniques,
  10. (2011). Contour detection and hierarchical image segmentation,”
  11. (2004). Efficient graph-based image segmentation,”
  12. (2012). Experimental validation of an unpowered unmanned aerial system: application to forced landing scenarios,”
  13. (2006). Eyes in the domestic sky: An assessment of sense and avoid technology for the army’s warrior unmanned aerial vehicle,”
  14. (2009). Forced landing technologies for unmanned aerial vehicles : towards safer operations,” in Aerial Vehicles,
  15. (2010). Guided chaos: path planning and control for a uav-forced landing,”
  16. (2008). Image segmentation evaluation: A survey of unsupervised methods,”
  17. (2004). Issues concerning integration of unmanned aerial vehicles in civil airspace,”
  18. (2001). Landing strategies in honeybees, and possible applications to autonomous airborne vehicles,”
  19. (2002). Mean shift: a robust approach toward feature space analysis,”
  20. (1998). Motion segmentation and tracking using normalized cuts,” in Computer Vision,
  21. (2011). Multiregion image segmentation by parametric kernel graph cuts,” Image Processing,
  22. (1997). Normalized cuts and image segmentation,” in Computer Vision and Pattern Recognition,
  23. (2010). Open source computer vision library,”
  24. (2011). Path planning, guidance and control for a uav forced landing,”
  25. (2010). The SAGE Handbook of Remote Sensing.
  26. (2001). Towards vision-based safe landing for an autonomous helicopter,”
  27. (2011). Unmanned systems integrated roadmap FY2011-2036. Office of the Secretary of Defense.
  28. (2009). Videography: IIDC capture software for linux,”
  29. (2010). Vision-based precision landings of a tailsitter uav,”
  30. (2003). Visually-guided landing of an unmanned aerial vehicle,”

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