Advertisement

Outdoor Localization of Quad-Rotor Using Extended Kalman Filter and Cell Division Algorithm

  • Yoon Ki Kim
  • Ki Jung Kim
  • Min Cheol Lee
  • Jang-Myung Lee
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 302)

Abstract

This paper proposes a new technique that produces the improved local information using low-cost GPS/INS system combined by Extended Kalman Filter and Cell Division Algorithm when a Quad-rotor flies. Throughout the research, the low-cost GPS is combined with INS by using extended Kalman filter in order to improve local information. However, this system has the disadvantage that the level of precision for the position information is influenced by the performance of GPS. In order to deal with such disadvantages, the algorithm based on cell division can be adopted. When the quad-rotor flies outdoor, it is possible to predict that its moving path is short, since all the short moving paths of the quad-rotor can be assumed to be straight. Cell division algorithm is used to make such a short moving path and determine the closest local information of the GPS/INS system. Through the above process, an improved kind of local information can be obtained when the quad-rotor flies. Also, the performance of the proposed system can be verified based on various outdoor experiments.

Keywords

Cell division algorithm Extended Kalman filter Quad-rotor GPS INS Localization 

Notes

Acknowledgments

This work was supported by the National Research Foundation of Korea(NRF) Grant funded by the Korean Government(MSIP) (NRF-2013R1A1A2021174). This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education (NRF-2010-0024129).

References

  1. 1.
    M.G.Kim, Y.D.Kim, “Multiple UAVs Nonlinear Guidance Laws for Stationary Target Observation with Waypoint Incidence Angle Constraint”, Int’l J. of Aeronautical & Space Sci, Vol. 14, No. 1, pp 67–74, 2013.Google Scholar
  2. 2.
    S. Kim, C. Roh, S. Kang, and M. Park, “Outdoor navigation of a mobile robot using differential GPS and curb detection”, Proceeding of IEEE International Conference on Robotics and Automation, 2007.Google Scholar
  3. 3.
    G. T. Schmidt, “INS/GPS Technology Trends”, NATO Research and Technology Organization, May 2009.Google Scholar
  4. 4.
    J.H. Seung, D.J., Lee, J.Y.Ryu, “Precise Positioning Algorithm Development for Quadrotor Flying Robots Using Dual Extended Kalman Filter”, Journal of Institute of Control, Robotics and System, Vol. 19, No. 2, pp 183–193, 2013.Google Scholar
  5. 5.
    D.J.Jwo, C.F.Yang, C.H.Chuang, T.Y.Lee, “Performance Enhancement for Ultra-tight GPS/INS integration using a fuzzy adaptive strong tracking unscented Kalman Filter”, Nonlinear Dynamics, Vol. 73, No.1 pp 377–395, 2013.Google Scholar
  6. 6.
    S. H. Choi, Y. K. KIM, “Outdoor Precision Position Estimation System Using Multiple GPS and EKF”, Journal of Korea Robotics Society, Vol. 8, No2, pp 129–135, 2013.Google Scholar
  7. 7.
    M.Rengarajan, G. Anitha, “Algorithm Development And Testing Of Low Cost Way Point Navigation System”, Engineering Science and Technology: An International Journal, Vol No. 2, pp 411–414, April 2013.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Yoon Ki Kim
    • 1
  • Ki Jung Kim
    • 1
  • Min Cheol Lee
    • 1
  • Jang-Myung Lee
    • 1
  1. 1.Department of Electrical EngineeringPusan National UniversityBusanKorea

Personalised recommendations