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Monocular Pose Estimation for an Unmanned Aerial Vehicle Using Spectral Features

  • Gastón AraguásEmail author
  • Claudio Paz
  • Gonzalo Perez Paina
  • Luis Canali
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 664)

Abstract

Pose estimation of Unmanned Aerial Vehicles (UAV) using cameras is currently a very active research topic in computer and robotic vision, with special application in GPS-denied environments. However, the use of visual information for ego-motion estimation presents several difficulties, such as features search, data association (feature correlation), inhomogeneous features distribution in the image, etc.

Notes

Acknowledgments

This work was partially funded by the Argentinean institutions Universidad Tecnológica Nacional through the project ‘Fusión Sensorial para Estimación de Posición y Orientación 3D”, UTN-PID-2155, and the National Agency for Science and Technology Promotion through the project “Autonomous Vehicle Guidance Fusing Low-cost GPS and other Sensors”, PICT-PRH-2009-0136, both currently under development at CIII, UTN, Córdoba, Argentina.

References

  1. 1.
    Angermann M, Frassl M, Doniec M, Julian B, Robertson P (2012) Characterization of the indoor magnetic field for applications in localization and mapping. In: 2012 International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1–9Google Scholar
  2. 2.
    Araguás G, Sánchez J, Canali L (2010) Monocular visual odometry using features in the fourier domain. In: VI Jornadas Argentinas de Robótica. Instituto Tecnológico de Buenos Aires, Buenos Aires, ArgentinaGoogle Scholar
  3. 3.
    Araguás G, Paz C, Gaydou D, Paina GP (2014) Quaternion-based orientation estimation fusing a camera and inertial sensors for a hovering UAV. J Intell RobotSyst 77(1):37–53CrossRefGoogle Scholar
  4. 4.
    Baker S, Matthews I (2004) Lucas-kanade 20 years on: a unifying framework: Part 1: the quantity approximated, the warp update rule, and the gradient descent approximation. Int J Comput Vis 56(3):221–255CrossRefGoogle Scholar
  5. 5.
    Bonin-Font F, Ortiz A, Oliver G (2008) Visual navigation for mobile robots: a survey. J Intell Robot Syst 53(3):263–296CrossRefGoogle Scholar
  6. 6.
    Corke P (2011) Robotics, vision and control, springer tracts in advanced robotics, vol 73. Springer, BerlinzbMATHGoogle Scholar
  7. 7.
    Faugeras O, Luong QT (2004) The geometry of multiple images: the laws that govern the formation of multiple images of a scene and some of their applications. MIT press, CambridgezbMATHGoogle Scholar
  8. 8.
    Fischler MA, Bolles RC (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM 24(6):381–395MathSciNetCrossRefGoogle Scholar
  9. 9.
    Hartley R, Zisserman A (2003) Multiple view geometry in computer vision. Cambridge University Press, CambridgezbMATHGoogle Scholar
  10. 10.
    Kaminski JY, Shashua A (2004) Multiple view geometry of general algebraic curves. Int J Comput Vis 56(3):195–219CrossRefGoogle Scholar
  11. 11.
    Kuglin CD, Hines DC (1975) The phase correlation image alignment method. Proc Int Conf Cybern Soc 4:163–165Google Scholar
  12. 12.
    Li B, Gallagher T, Dempster A, Rizos C (2012) How feasible is the use of magnetic field alone for indoor positioning? In: 2012 International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1–9Google Scholar
  13. 13.
    Ma Y, Soatto S, Kosecká J, Sastry SS (2010) An invitation to 3-d vision: from images to geometric models. Springer, New YorkzbMATHGoogle Scholar
  14. 14.
    Scaramuzza D, Achtelik M, Doitsidis L, Friedrich F, Kosmatopoulos E, Martinelli A, Achtelik M, Chli M, Chatzichristofis S, Kneip L, Gurdan D, Heng L, Lee GH, Lynen S, Pollefeys M, Renzaglia A, Siegwart R, Stumpf J, Tanskanen P, Troiani C, Weiss S, Meier L (2014) Vision-controlled micro flying robots: from system design to autonomous navigation and mapping in GPS-Denied environments. IEEE Robot Autom Mag 21(3):26–40CrossRefGoogle Scholar
  15. 15.
    Shi J, Tomasi C (1994) Good features to track. In: 1994 IEEE computer society conference on computer vision and pattern recognition, 1994. Proceedings CVPR’94, pp. 593–600Google Scholar
  16. 16.
    Weiss S, Achtelik MW, Lynen S, Achtelik MC, Kneip L, Chli M, Siegwart R (2013) Monocular vision for long-term micro aerial vehicle state estimation: a compendium. J Field Robot 30(5):803–831CrossRefGoogle Scholar
  17. 17.
    Zitová B, Flusser J (2003) Image registration methods: a survey. Image Vis Comput 21(11):977–1000CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Gastón Araguás
    • 1
    Email author
  • Claudio Paz
    • 1
  • Gonzalo Perez Paina
    • 1
  • Luis Canali
    • 1
  1. 1.Centro de Investigación en Informática para la Ingeniería (CIII), Facultad Regional CórdobaUniversidad Tecnológica NacionalCórdobaArgentina

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