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Determinig of an object orientation in 3D space using direction cosine matrix and non-stationary Kalman filter

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Języki publikacji
EN
Abstrakty
EN
This paper describes a method which determines the parameters of an object orientation in 3D space. The rotation angles calculation bases on the signals fusion obtained from the inertial measurement unit (IMU). The IMU measuring system provides information from a linear acceleration sensors (accelerometers), the Earth’s magnetic field sensors (magnetometers) and the angular velocity sensors (gyroscopes). Information about the object orientation is presented in the form of direction cosine matrix whose elements are observed in the state vector of the non-stationary Kalman filter. The vector components allow to determine the rotation angles (roll, pitch and yaw) associated with the object. The resulting waveforms, for different rotation angles, have no negative attributes associated with the construction and operation of the IMU measuring system. The described solution enables simple, fast and effective implementation of the proposed method in the IMU measuring systems
Rocznik
Strony
223--244
Opis fizyczny
Bibliogr. 27 poz., rys., wykr., wzory
Twórcy
autor
  • nstitute of Automatic Control, Silesian University of Technology, Akademicka 16 Street, 44-100 Gliwice, Poland
autor
  • nstitute of Automatic Control, Silesian University of Technology, Akademicka 16 Street, 44-100 Gliwice, Poland
Bibliografia
  • [1] D. H. Titterton and J. L. Weston: Strapdown Inertial Navigation Technology. The Institution of Electrical Engineers, 2nd ed., 2004.
  • [2] M. S. Grewal, L. R. Weill and A. P. Andrews: Global Positioning Systems, Inertial Navigation, and Integration. John Wiley & Sons, 2001.
  • [3] M. Gucma and J. Montewka: Podstawy morskiej nawigacji inercyjnej. Szczecin: Akademia Morska w Szczecinie, 2006. (in Polish).
  • [4] S. M. Lavalle: Planning Algorithms. Cambridge University Press, 2006.
  • [5] M. Euston, P. Coote, R. Mahony, J. Kim and T. Hamel: A complementary filter for attitude estimation of a fixed-wing UAV. In Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), (2008), 340-345.
  • [6] J. Kim: Autonomous Navigation for Airborne Applications. PhD thesis, Australian Centre for Field Robotics, The University of Sydney, 2004.
  • [7] R. M. Murray, Z. Li and S. S. Sastry: A Mathematical Introduction to Robotic Manipulation. CRC Press, 1994.
  • [8] D. Rotenberg: Inertial and Magnetic Sensing of Human Motion. PhD thesis, University of Twente, 2006.
  • [9] E. R. Bachmann: Inertial and magnetic tracking of limb segment orientation for inserting humans into synthetic environments. PhD thesis, Naval Postgraduate School Monterey, California, 2000.
  • [10] D. Roetenberg, J. Henk H. J. Luinge, T. Chris, C. T. M. Baten and P. H. Veltink. Compensation of magnetic disturbances improves inertial and magnetic sensing of human body segment orientation. IEEE Trans. Neural Systems and Rehabilitation Engineering, 13(3), (2005), 395-405.
  • [11] J. Pusa: Strapdown inertial navigation system aiding with nonholonomic constraints using indirect Kalman filtering. Master’s thesis, Tampere University of Technology, 2009.
  • [12] S. Rönnback: Developement of a ins/gps navigation loop. Master’s thesis, Lulea University of Technology, 2000.
  • [13] P. Sadłowski: Parametryzacje rotacji i algorytmy rozwiązywania równań dynamiki z rotacyjnymi stopniami swobody. PhD thesis, Polskia Akademia Nauk, 2007. (in Polish).
  • [14] T. Hamel and R. Mahony: Attitude estimation on so(3) based on direct inertial measurements. In Proc. of the IEEE Int. Conf. on Robotics and Automation (ICRA), (2006), 2170-2175.
  • [15] J. F. Guerrero-Castellanos, H. Madrigal-Sastre, S. Durand, L. Torres and G. A. Munoz Hernández: A robust nonlinear observer for real-time attitude estimation using low-cost mems inertial sensors. Sensors, 13(11), (2013), 15138-15158.
  • [16] S. Fux: Development of a planar low cost inertial measurement unit for uavs and mavs. Master’s thesis, Eidgenössische Technische Hochschule Zürich, 2008.
  • [17] K. Jaskot and A. Babiarz: The inertial measurement unit for detection of position. Przegla˛d Elektrotechniczny, 86(11a), (2010), 323-333.
  • [18] M. Pedley: Tilt Sensing Using a Three-Axis Accelerometer. Freescale Semiconductor Application Note, rev.5 ed., 2013. n.AN3461.
  • [19] K. Kim and C. G. Park: A new initial alignment algorithm for strapdown inertial navigation system using sensor output. In Proc. of the 17th World Congress. The International Federation of Automatic Control (IFAC), (2008), 13034-13039.
  • [20] T. Ozyagcilar: Implementing a Tilt-Compensated eCompass using Accelerometer and Magnetometer Sensors. Freescale Semiconductor Application Note, rev.3 ed., 2012. n.AN4248.
  • [21] O. J. Woodman: An Introduction to Inertial Navigation,” Tech. Rep. 696, University of Cambridge, 2007.
  • [22] S. Ayub, A. Bahraminisaab and B. Honary: A sensor fusion method for smart phone orientation estimation. In 13th Annual Post Graduate Symp. on the Convergence of Telecommunications, Networking and Broadcasting, (2012).
  • [23] R. Mahony, T. Hamel and J.-M. Pflimlin. Non-linear complementary filters on the special orthogonal group. IEEE Trans. on Automatic Control, 53(3), (2008), 1203-1217.
  • [24] R. Grygiel, R. Bieda and K. Wojciechowski: Angles from gyroscope to complementary filter in IMU. Przegla˛d Elektrotechniczny, 90(9), (2014), 217-224.
  • [25] E. Foxlin: Inertial head-tracker sensor fusion by a complementary separate-bias kalman filter. In Proc. of IEEE Virtual Reality Annual International Symp., (1996), 185-194.
  • [26] X. Kong: Ins algorithm using quaternion model for low cost IMU. Robotics and Autonomous Systems, 46, (2004), 221-246.
  • [27] L. Cong, E. Li, H. Qin, K. V. Ling and R. Xue: A performance improvement method for low-cost land vehicle gps/mems-ins attitude determination. Sensors, 15(3), (2015), 5722-5746.
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę
Typ dokumentu
Bibliografia
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