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Image Processing for UAV Autonomous Navigation Applying Self-configuring Neural Network

  • Gerson da Penha Neto
  • Haroldo F. de Campos VelhoEmail author
  • Elcio H. Shiguemori
  • José Renato G. Braga
Chapter

Abstract

Application and development of Unmanned Aerial Vehicles (UAV) have had a rapid growth. The flight control of these aircrafts can be performed remotely or autonomously. There are different strategies for the UAV autonomous navigation. The positioning estimation can be done by using inertial sensors and General Navigation Satellite Systems (GNSS). The use of the GNSS signal can present some difficulties: natural or not natural interference. An alternative for positioning adjustment is to use a data fusion from different sensors by a Kalman filter. A supervised artificial network (ANN) is trained to emulate the filter for reducing the computational effort. An automatic best topology for the neural network is obtained by minimizing a functional by a new meta-heuristic called Multi-Particle Collision Algorithm (MPCA). Our results show similar accuracy between the ANN and the Kalman filter, with better processing performance to the neural network.

Notes

Acknowledgements

The authors would like to thank the FAPESP and CNPq, Brazilian agencies for research support.

References

  1. [AnEtAl13]
    Anochi, J. A., Sambatti, S. B., Luz, E. F. P. d., and Campos Velho, H. F. d.: New learning strategy for supervised neural network: Mpca meta-heuristic approach. In Anais…, Congresso Brasileiro de Inteligência Computacional, (CBIC)., Sociedade Brasileira de Inteligência Computacional, 01, pp. 01–06 (2013).Google Scholar
  2. [BeVo07]
    Benardos, P. G. and Vosniakos, G. C.: Optimizing feedforward artificial neural network architecture. Eng. Appl. Artif. Intell, 20, pp. 365–382 (2007).CrossRefGoogle Scholar
  3. [BiEtAl17]
    Birdal, A. C., Avdan, U., and Târk, T.: Estimating tree heights with images from an unmanned aerial vehicle. Geomatics, Natural Hazards and Risk, 2, pp. 1144–1156 (2017)CrossRefGoogle Scholar
  4. [BrEtAl15]
    Braga, J. R. G., Campos Velho, H. F., and Shiguemori, E. H.: Estimation of UAV position using LiDAR images for autonomous navigation over the ocean. In 9th International Conference on Sensing Technology (ICST), pp. 811–816 (2015).Google Scholar
  5. [BrEtAl16]
    Braga, J. R. G., Campos Velho, H. F., Conte, G., Doherty, P., and Shiguemori, E. H.: An image matching system for autonomous UAV navigation based on neural network. In 14th International Conference on Control, Automation, Robotics and Vision (ICARCV), pp. 1–6 (2016).Google Scholar
  6. [CoDo08]
    Conte, G. and Doherty, P. An integrated UAV navigation system based on aerial image matching. In IEEE Aerospace Conference, pp. 1–10 (2008).Google Scholar
  7. [CoDo09]
    Conte, G. and Doherty, P. Vision-based unmanned aerial vehicle navigation using geo-referenced information. EURASIP Journal on Advances in Signal Processing, 1, 387–308 (2009).zbMATHGoogle Scholar
  8. [SiEtAl15]
    Silva, W., Shiguemori, E. H., Vijaykumar, N. L., and Campos Velho, H. F.: Estimation of UAV position with use of thermal infrared images. In 9th International Conference on Sensing Technology (ICST), pp. 828–833 (2015).Google Scholar
  9. [DaEtAl17]
    Dash, J. P., Watt, M. S., Pearse, G. D., Heaphy, M., and Dungey, H. S.: Assessing very high resolution UAV imagery for monitoring forest health during a simulated disease outbreak. ISPRS Journal of Photogrammetry and Remote Sensing, 131, pp. 1–14 (2017).CrossRefGoogle Scholar
  10. [FaEtAl18]
    Faria, L., Augusto, M. S., Correia, A. F., M., and Roso, N.: Susceptibility of GPS-dependent complex systems to spoofing, In Journal of Aerospace Technology and Management, Sâo José dos Campos - SP (2018), vol. 10.Google Scholar
  11. [FiEtAl17]
    Fiori, L., Doshi, A., Martinez, E., Orams, M. B., and Bollard-Breen, B.: The use of unmanned aerial systems in marine mammal research. Remote Sensing, 9 (2017).CrossRefGoogle Scholar
  12. [GoRe15]
    Goncalves, J. and Renato, H.: UAV photogrammetry for topographic monitoring of coastal areas. In ISPRS Journal of Photogrammetry and Remote Sensing, vol. 104, pp. 101–111 (2015).CrossRefGoogle Scholar
  13. [Gr15]
    Groves, P. D.: Principles of GNSS, inertial, and multisensor integrated navigation systems, 2nd Ed. IEEE Aerospace and Electronic Systems Magazine, vol. 30, pp. 26–27 (2015).CrossRefGoogle Scholar
  14. [Ha07]
    Haykin, S.: Neural networks: principles and practical. Artmed (2007).Google Scholar
  15. [JaEtAl17]
    James, M. R., Robson, S., d’Oleire-Oltmanns, S., and Niethammer, U.: Optimising UAV topographic surveys processed with structure-from-motion: Ground control quality, quantity and bundle adjustment. Geomorphology, vol. 280, pp.51–66 (2017).CrossRefGoogle Scholar
  16. [LoEtAl12]
    Loghmanian, S. M. R., Jamaluddin, H., Ahmad, R., Yusof, R., and Khalid, M.: Structure optimization of neural network for dynamic system modeling using multiobjective genetic algorithm. Neural Computing and Applications, vol. 21, pp. 1281–1295 (2012).CrossRefGoogle Scholar
  17. [Ma02]
    Maybeck, P.: Stochastic Models, Estimation, and Control - vol. 1, Academic Press (2002).Google Scholar
  18. [MiEtAl02]
    Michaelsen, E., Kirchhof, M., and Stilla, U. Stilla.: Sensor pose inference from airborne videos by decomposing homography estimates. In Accepted for ISPRS (2004).Google Scholar
  19. [Sa05]
    Sacco, W.: A new stochastic optimization algorithm based on a particle collision metaheuristic (2005).Google Scholar
  20. [SaEtAl12a]
    Sambatti, S. B. M., Anochi, J. A., Luz, E. F. P. d., Carvalho, A. R., Shiguemori, E. H., and Campos Velho, H. F.: Mpca meta-heuristics for automatic architecture optimization of a supervised artificial neural network. In Proceedings…World Congress on Computational Mechanics, 10 (2012).Google Scholar
  21. [SaEtAl12b]
    Sambatti, S. B. M., Furtado, H. C. M., Anochi, J. A., Luz, E. F. P. d., and Campos Velho, H. F.: Automatic configuration of an artificial neural network with application to data assimilation. In Proceedings…International Conference on Integral Methods in Science and Engineering, 12 (2012).Google Scholar
  22. [SaEtAl16]
    Sambatti, S. B. M., Campos Velho, H. F., Furtado, H. C. M., Gomes, V. C., and Charão, A. S.: Self-configured neural network for data assimilation using FPGA for ocean circulation. Conference of Computational Interdisciplinary Sciences, 4 (2016).Google Scholar
  23. [VaVa14]
    Valavanis, K. P. and Vachtsevanos, G. J.: Handbook of Unmanned Aerial Vehicles. Springer Publishing Company, Incorporated (2014).zbMATHGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Gerson da Penha Neto
    • 1
  • Haroldo F. de Campos Velho
    • 1
    Email author
  • Elcio H. Shiguemori
    • 2
  • José Renato G. Braga
    • 1
  1. 1.Instituto Nacional de Pesquisas Espaciais (INPE)São José dos CamposBrazil
  2. 2.Departamento de Ciência e Tecnologia Aeroespacial (DCTA)São José dos CamposBrazil

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