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Latin American Workshop on Computational Neuroscience

LAWCN 2017: Computational Neuroscience pp 94–105Cite as

Developing of a Video-Based Model for UAV Autonomous Navigation

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 720))

Abstract

The use of unmanned aerial vehicles (UAVs) has driven the research and development of multiple applications. Autonomous and cognitive navigation in remote environments requires the use of independent on board sensors. One advantage of these vehicles is that they have an on-board camera that allows them to collect visual information about the environment. This work shows a way to be aware of the UAV movement depending only on images. Therefore, a vision-based mathematical model was defined that describes the movement. System identification experiments and results are presented to verify the mathematical model structure and to identify model parameters comparing with state of art models. Finally a visual-based model compare with other methods and improve performance.

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References

  1. Limnaios, G.: Current usage of unmanned aircraft systems (UAS) and future challenges: a mission oriented simulator for UAS as a tool for design and performance evaluation. J. Comput. Model 4(1), 167–188 (2014)

    Google Scholar 

  2. Dickerson, L.: UAV on the rise. In: Aviation Week & Space Technology. de Aerospace Source Book, vol. 166, McGraw Hill, New York (2007)

    Google Scholar 

  3. Visiongain: The unmanned aerial vehicles (UAV) market 2009–2019. Londres, (2009)

    Google Scholar 

  4. Whitten, W.D.: Improving the robustness of monocular vision-aided navigation for multirotors through integrated estimation and guidance. Doctoral dissertation (2017)

    Google Scholar 

  5. Ma, Y., Soatto, S., Kosecka, J., Sastry, S.S.: An Invitation to 3-D Vision: From Images to Geometric Models, vol. 26. Springer Science & Business Media, New York (2012)

    MATH  Google Scholar 

  6. Wilbert, G., Aguilar, C.A.: Compensación y Aprendizaje de Efectos Generados en la Imagen durante el Desplazamiento de un Robot. In: X Simposio CEA de Ingeniería de Control, pp. 165–170 (2012)

    Google Scholar 

  7. Aguilar, W.G., Angulo, C.: Real-time model-based video stabilization for microaerial vehicles. Neural Process. Lett. 43(2), 459–477 (2016)

    Article  Google Scholar 

  8. Guenard, N., Hamel, T., Mahony, R.: A practical visual servo control for an unmanned aerial vehicle. IEEE Trans. Robot. 24(2), 331–340 (2008)

    Article  Google Scholar 

  9. Aguilar, W., Angulo, C.: Robust video stabilization based on motion intention for low-cost micro aerial vehicles. In: A: International Multi-conference on Systems, Signals and Devices. B: Proceedings of 11th International Multi-conference on Systems, Signals and Devices (SSD 2014). Castelldefels: 2014, pp. 1–6 (2014)

    Google Scholar 

  10. Ganci, S.: Looking through a pinhole: physical and physiological phenomena. J. Photon. Opt. Technol. 3(2), 13–16 (2017)

    Google Scholar 

  11. Wu, A., Johnson, E.N., Kaess, M., Dellaert, F., Chowdhary, G.: Autonomous flight in GPS-denied environments using monocular vision and inertial sensors. J. Aerospace Inf. Sys. 10(4), 172–186 (2013)

    Article  Google Scholar 

  12. Chowdhary, G., Johnson, E.N., Magree, D., Wu, A., Shein, A.: GPS-denied indoor and outdoor monocular vision aided navigation and control of unmanned aircraft. J. Field Robot. 30(3), 415–438 (2013)

    Article  Google Scholar 

  13. Nixon, M., Aguado, A.S.: Feature Extraction & Image Processing, 2nd edn. Academic Press, Cambridge (2008)

    Google Scholar 

  14. Derpanis, K.G.: Overview of the RANSAC algorithm. Image Rochester NY 4(1), 2–3 (2010)

    Google Scholar 

  15. Kendoul, F.: Survey of advances in guidance, navigation, and control of unmanned rotorcraft systems. J. Field Robot. 29(2), 315–378 (2012)

    Article  Google Scholar 

  16. Wilbert, G., Aguilar, C.A.: Compensación de los efectos generados en la imagen por el control de navegación del robot Aibo ERS 7. In: Conference: VII Congreso de Ciencia y Tecnología ESPE, At Sangolquí (2012)

    Google Scholar 

  17. Sayem, A.S.S.: Vision-Aided Navigation for Autonomous Vehicles Using Tracked Feature Points (2016)

    Google Scholar 

  18. Zhang, X., Wang, X., Yuan, X., Wang, S.: An improved SIFT algorithm in the application of close-range stereo image matching. In: IOP Conference Series: Earth and Environmental Science, vol. 46, no. 1, p. 012009. IOP Publishing (2016)

    Google Scholar 

  19. Al-khafaji, S.L., Zhou, J., Zia, A., Liew, A.W.C.: Spectral-spatial scale invariant feature transform for hyperspectral images. IEEE Trans. Image Process. (2017)

    Google Scholar 

  20. Lowe, D.: Object recognition from local scale-invariant features. In: Proceedings of IEEE International Conference on Computer Vision, vol. 2, pp. 1150–1157 (1999)

    Google Scholar 

  21. Zhu, Y., Shen, X., Chen, H.: Copy-move forgery detection based on scaled ORB. Multimedia Tools Appl. 75(6), 3221–3233 (2016)

    Article  Google Scholar 

  22. Xie, S., Zhang, W., Ying, W., Zakim, K.: Fast detecting moving objects in moving background using ORB feature matching. In: 2013 Fourth International Conference on Intelligent Control and Information Processing (ICICIP), pp. 304–309. IEEE (2013)

    Google Scholar 

  23. Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: an efficient alternative to SIFT or SURF. In: ICCV (2011)

    Google Scholar 

  24. Aguilar, W.G., Casaliglla, V.P., Pólit, J.L.: Obstacle avoidance based-visual navigation for micro aerial vehicles. Electronics 6, 10 (2017)

    Article  Google Scholar 

  25. Harris, C., Stephens, M.: A combined corner and edge detector. In: Alvey Vision Conference, pp. 147–151 (1988)

    Google Scholar 

  26. Torr, P., Zisserman, A.: MLESAC: a new robust estimator with application to estimating image geometry. Comput. Vis. Image Underst. 78, 138–156 (2000)

    Article  Google Scholar 

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Acknowledgments

This work is part of the project “Perception and localization system for autonomous navigation of rotor micro aerial vehicle in GPS-denied environments (VisualNav Drone)” from the Centro de Investigación Científica y Tecnológica del Ejército (CICTE), directed by Wilbert G. Aguilar.

Author information

Authors and Affiliations

  1. Departamento de Seguridad y Defensa DESP, Universidad de las Fuerzas Armadas ESPE, Sangolquí, Ecuador

    Wilbert G. Aguilar

  2. Departamento de Eléctrica y Electrónica DEEE, Universidad de las Fuerzas Armadas ESPE, Sangolquí, Ecuador

    Vinicio S. Salcedo, David S. Sandoval & Bryan Cobeña

  3. Centro de Investigación Científica y Tecnológica del Ejército CICTE, Universidad de las Fuerzas Armadas ESPE, Sangolquí, Ecuador

    Wilbert G. Aguilar, Vinicio S. Salcedo, David S. Sandoval & Bryan Cobeña

  4. Research Group on Knowledge Engineering GREC, Universitat Politècnica de Catalunya UPC-BarcelonaTech, Barcelona, Spain

    Wilbert G. Aguilar

Authors
  1. Wilbert G. Aguilar
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  2. Vinicio S. Salcedo
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  3. David S. Sandoval
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  4. Bryan Cobeña
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Contributions

Wilbert G. Aguilar directed the research; Wilbert G. Aguilar, Vinicio S. Salcedo, David Sandoval and Bryan Cobeña designed the experiments; Vinicio S. Salcedo, David Sandoval and Bryan Cobeña implemented and performed the experiments; Wilbert G. Aguilar, Vinicio S. Salcedo, David Sandoval and Bryan Cobeña analyzed the results. The authors wrote and revised the paper

Corresponding author

Correspondence to Wilbert G. Aguilar .

Editor information

Editors and Affiliations

  1. Federal University of Rio Grande do Sul, Porto Alegre, Brazil

    Dante Augusto Couto Barone

  2. Federal Institute of Education, Science and Technology of Bahia, Camacari, Brazil

    Eduardo Oliveira Teles

  3. Federal Institute of Education, Science and Technology Farroupilha, Santa Maria, Brazil

    Christian Puhlmann Brackmann

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The authors declare no conflict of interest.

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Aguilar, W.G., Salcedo, V.S., Sandoval, D.S., Cobeña, B. (2017). Developing of a Video-Based Model for UAV Autonomous Navigation. In: Barone, D., Teles, E., Brackmann, C. (eds) Computational Neuroscience. LAWCN 2017. Communications in Computer and Information Science, vol 720. Springer, Cham. https://doi.org/10.1007/978-3-319-71011-2_8

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  • DOI: https://doi.org/10.1007/978-3-319-71011-2_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-71010-5

  • Online ISBN: 978-3-319-71011-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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