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Inertial navigation position and orientation estimation with occasional Galileo satellite position fixes and stereo camera measurements

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Treść / Zawartość
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper an adaptive unscented Kalman filter based mixing filter is used to integrate kinematic satellite aided inertial navigation system with vision based measurements of five representative points on a runway in a modern receiver that incorporates carrier phase smoothing and ambiguity resolution. Using high resolution multiple stereo camera based measurements of five points on the runway, in addition to a set of typical pseudo-range estimates that can be obtained from a satellite navigation system such GPS or GNSS equipped with a carrier phase receiver, the feasibility of generating high precision estimates of the typical outputs from an inertial navigation system is demonstrated. The methodology may be developed as a stand-alone system or employed in conjunction with a traditional strapped down inertial navigation systems for purposes of initial alignment. Moreover the feasibility of employing adaptive mixing was explored as it facilitates the possibility of using the system for developing a vision based automatic landing controller.
Rocznik
Strony
131--153
Opis fizyczny
Bibliogr. 26 poz., rys.
Twórcy
autor
  • Queen Mary, University of London
autor
  • Queen Mary, University of London
Bibliografia
  • [1] Andrade-Cetto J., Sanfeliu A., The effects of partial observability in SLAM, Proc. of the 2004 IEEE International Conference on Robotics and Automation, pp. 397-402, New Orleans, LA, 2004.
  • [2] Bourquardez O., Chaumette F., Visual Servoing of an Airplane for Alignment with respect to a Runway, IEEE Int. Conf. on Robotics and Automation, ICRA 2007, pp. 1330-1335.
  • [3] Dellago R., Detoma E., Spazio A., Galileo-GPS Interoperability and Compatibility: A Synergetic Viewpoint, Proceedings of the ION GPS, 2003.
  • [4] Dellago R., Pieplu J. M., Stalford R., The Galileo System Architecture at the End of the Design Phase, ION GPS/GNSS, September 2003.
  • [5] Espiau B., Chaumette F., Rives P., A New Approach to Visual Servoing in Robotics, IEEE Transactions on Robotics and Automation, 1992, Vol. 8, No. 3, pp. 313-325.
  • [6] Guenter W., Hein J. et al., Status of Galileo Frequency and Signal Design, Proc. of ION GPS, 2002.
  • [7] Ho C-C. J., McClamroch N. H., Automatic Spacecraft Docking Using Computer Vision-Based Guidance and Control Techniques, Journal of Guidance, Control, and Dynamics, 1993, Vol. 16, No. 2, pp. 281-288.
  • [8] Julier S. J., The Scaled Unscented Transformation, Proceedings of the American Control Conference, 2002, Vol. 6, pp. 4555-4559.
  • [9] Kelly R., Robust asymptotically stable visual servoing of planar robots, IEEE Trans. on Robotics and Automation, 1996, Vol. 12, No. 5, pp. 759-766.
  • [10] Kelly R., Carelli R., Nasisi O., Kuchen B., Reyes F., Stable Visual Servoing of Camera-in-Hand Robotic Systems, IEEE/ASME Transactions on Mechatronics, 2000, Vol. 5, No. 1.
  • [11] Kim J., Sukkarieh S., Autonomous airborne navigation in unknown terrain environments, IEEE Transactions on Aerospace and Electronic Systems, 2004, 40(3), pp. 1031-1045.
  • [12] Langelaan J. W., State Estimation for Autonomous Flight in Cluttered Environments, PhD thesis, Stanford University, Department of Aeronautics and Astronautics, 2006,
  • [13] Malis E., Vision-based control invariant to camera intrinsic parameters: stability analysis and path tracking, IEEE International Conference on Robotics and Automation, Washington, D.C., USA, 2002.
  • [14] Malis E., Chaumette F., Boudet, S., 2 1/2 d visual servoing, IEEE Trans. on Robotics and Automation, 1999, Vol. 15, No. 2, pp. 234-246.
  • [15] Malis E., Chaumette F., Boudet S., 2 1/2 d visual servoing with respect to unknown objects through a new estimation scheme of camera displacement, International Journal of Computer Vision, 2000, Vol. 37, No. 1, pp. 79-97.
  • [16] Navarro-Reyes D., Galileo Programme Status and ongoing GIOVE Experimentation, European Geosciences Union (EGU), Vienna, Austria, 2007.
  • [17] Roumeliotis S., Johnson A., Montgomery J., Augmenting inertial navigation with image-based motion estimation, IEEE International Conference on Robotics and Automation (ICRA), p. 4326-33, Washington, D.C., USA, 2002.
  • [18] Savage P., Strapdown inertial navigation integration algorithm design, part 1, Attitude algorithms, Journal of Guidance, Control and Dynamics, 1998, Vol. 21, No. 1, pp. 19-28.
  • [19] Savage P., Strapdown inertial navigation integration algorithm design, part 2: Velocity and position algorithms, Journal of Guidance, Control and Dynamics, 1998, Vol. 21, No. 3, pp. 208-221.
  • [20] Shakernia O., Ma Y., Koo T. J., Sastry S., Landing an Unmanned Air Vehicle: Vision Based Motion Estimation and Nonlinear Control, Asian Journal of Control, 1999, Vol. 1, No. 3, pp. 128-145.
  • [21] Shang J., Shi Z., Vision-based Runway Recognition for UAV Autonomous Landing, IJCSNS International Journal of Computer Science and Network Security, 2007, Vol. 7, No. 3, pp. 112-117.
  • [22] Sharp C. S., Shakernia O., Sastry S. S., A Vision System for Landing an Unmanned Aerial Vehicle, Proceedings of the IEEE, 2001.
  • [23] Song Q., Qi J., Han J., An Adaptive UKF Algorithm and Its Application in Mobile Robot Control, ROBIO ’06, IEEE International Conference on Robotics and Biomimetics, Kunming, China, 2006, pp. 1117-1122.
  • [24] Strelow D., Motion estimation from image and inertial measurements, PhD thesis, Carnegie Mellon University, 2004.
  • [25] Vepa R., Zhahir A., High-Precision Kinematic Satellite and Doppler Aided Inertial Navigation System, The Journal of Navigation, 2011, Vol. 64, No. 1, pp. 91-108.
  • [26] Wu A., Johnson E., Proctor A., Vision-aided inertial navigation for flight control, Proceedings of the AIAA Guidance, Navigation, and Control Conference, number AIAA 2005-5998, San Francisco, CA, 2005.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-66b3affe-b2ab-41b5-b451-f317dacb0d83
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