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Gps ins odometer data fusion

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Gps ins odometer data fusion

  2. 2. Authors and Reference 2 Authors 1. M. AFTATAH 3. A. ABOUNADA 2. A. LAHRECH 4. A. SOULHI Reference International Journal of Automotive Technology Book: 1.Autonomous Mobile Robots_ Sensing,Control, Decision Making and Applications- (Ge, Shuzhi Sam) CRC Press (2006) 2. Inertial Navigation Systems with Geodetic Applications- (Christopher Jekeli, De Gruyter) (2000)
  3. 3. Outline 3  Objectives  Introduction  Comparison with Previous Work  Block diagram of Proposed Method  INS Modeling  Odometer Modeling  System model  Observation Model  Results  Summary
  4. 4. Objectives 4  To make a new fusion approach of two sensors that are the Inertial Navigation System (INS) and the odometer with Global Positioning System (GPS).  To make sure of continuous vehicle localization even if there is no GPS coverage.  To compare the result of INS sensor with the INS & odometer fusion approach when GPS is not available.  Introduce the Kalman filter for the vehicle localization
  5. 5. Introduction 5  Global positioning system (GPS) is well known system to find the position of the desired vehicle. GPS has some limitations.  It is subject to signal jamming  It cannot be used indoor  GPS has low update rate and is therefore not suitable for high-speed tracking
  6. 6. Introduction 6  Inertial navigation system has accelerometer and gyroscope.  INS is a system that delivers the position, velocity, and attitude of a vehicle by exploiting the output of inertial sensors. Here the attitude indicates the orientation of the mobile or in other words angular rates. Limitations  The measurements of the inertial sensors are affected by errors due to physical limitations. (bearings are not frictionless)  It also suffer from integration drift: small errors in the measurement of acceleration and angular velocity are integrated into progressively larger errors in velocity, which are compounded into still greater errors in position  So, only INS measurement cannot be trusted for navigation.
  7. 7. Introduction 7 Integration of INS and GPS The GPS position and velocity estimates are used as the initial conditions for the INS state during the next period of integration. In this approach, the position and velocity computed by the GPS receiver as measurements for the state estimation process For the fusion of this two measurement Kalman filter is used. But what will happen if GPS is not available for a period of time??
  8. 8. Introduction 8  The paper proposed a method is based on the use of an additional aiding source which is the odometer sensor.  Odometer is a standard component in Antilock Braking Systems (ABS) considered as a wheel speed sensor.  It is used when GPS is not available.  An odometer measures and displays the distance travelled by a vehicle by sensing the rotations of a wheel.  And Kalman filter is used for the fusion of the INS and odometer measurement.
  9. 9. Comparison with Previous Work 9 Previous Work Nav1 Nav2 Nav1 Nav2 Present Work
  10. 10. Block diagram of Proposed Method 10
  11. 11. INS Modeling 11  All coordinate frames are defined by orthogonal axes in a right-handed sense.  The body frame is attached to and moves with the vehicle. The inertial measurements are resolved along the axes of the platform frame.  To simplify the discussion, we assume that the body and platform frames are identical.  In this work these equations are expressed in the ENU frame (East, North and Up).  In the following the ENU coordinate system will be considered as the navigation frame (n-frame).  The body frame (b-frame) is defined at the INS center.
  12. 12. INS Modeling 12 The dynamic equations in the n-frame are given by following equations
  13. 13. INS Modeling 13
  14. 14. INS Modeling 14 Where, Rm and Rn are the radii of curvature in the meridian and prime vertical. -Prime meridian is 0 degree longitude. -the prime vertical is the vertical circle passing east and west through the zenith, and intersecting the horizon in its east and west points.
  15. 15. INS Modeling 15 is the eccentricity of the Earth, a and b are the semi-major axis and semi-minor axis of the Earth In equation (1)
  16. 16. INS Modeling  Eventually, the INS error equations are obtained by perturbing the kinematic equations, i.e. equations (1-3).  These error equations are used in the construction of the GPS/INS/Odometer system model. 16
  17. 17. INS Modeling 17 Where, Re is the radius of the earth and g is the gravity
  18. 18. INS Modeling 18 Where,
  19. 19. INS Modeling 19
  20. 20. INS Modeling 20 Block diagram representation of a strapdown INS.
  21. 21. Odometer Modeling 21 Abuhadrous, 2005
  22. 22. Odometer Modeling 22 Where, Lahrech, Boucher and Noyer, 2004; Lahrech, Boucher and Noyer, 2005 - rR and rL are the radii of the right and left wheels respectively. - e is the distance separating the two points of contact of the wheels with the ground.
  23. 23. Odometer Modeling 23  The discrete form of the equations used to obtain position and heading angle from odometer measures can be expressed as (Abuhadrous, 2005): Where, -xk and yk denote the position in the center of the axis, -ψOdo is the heading angle and ∆t is the sampling time.
  24. 24. System model 24  A KF is chosen to fuse the measurements of the INS and GPS when sufficient satellite signals are available and to integrate INS measures with odometer in the opposite case. Two main steps. (a)The first consists in predicting the state based on the system model. (b) The second is dedicated to update the state based on the measurements.
  25. 25. System model 25  The INS error equations are used in the Kalman filter as the system error dynamic model of integrated navigation: Where, is the state vector - δrn is the position errors, δvn is the linear velocity errors. - ε is the Euler angles errors. - are the biases of accelerometers and gyroscopes
  26. 26. System model 26  The drift of these biases can be modeled as a first-order Gauss-Markov process (Hou, 2004) represented as follow: Where i=x, y, z, is the correlation times for the accelerometers, is the correlation times for the gyroscopes.
  27. 27. System model 27 are the Gauss-Markov process driving noises
  28. 28. System model 28 Where,
  29. 29. System model 29  The discrete form of the system model can be written as: Where ∆t is the sampling time.
  30. 30. Observation Model 30 GPS measurements :  When the GPS signals are available, position and velocity solution from GPS are integrated with INS at the rate of 1 Hz.  The measurement model for GPS/INS loosely coupled scheme is: Where, n is the size of the state vector. covariance matrix
  31. 31. Observation Model 31 Odometer measurements : - integrated with the INS at the rate of 1 Hz
  32. 32. Results 32 SIMULATION RESULTS: - Time of simulation 26 minutes and 37 seconds. - Three zones (70 sec each) where the GPS signals are not available  This paper traits the performance of the proposed method in terms of accuracy using both simulated and real data.
  33. 33. Results 33 SIMULATION RESULTS: - During the GPS outages, the filter uses the odometer measures to correct the errors affecting the inertial sensors. - This error is approximately 0.005 meters/second in the presence of GPS signal blockages.
  34. 34. Results 34 SIMULATION RESULTS: - the error does not exceed 0.3 meters - The position errors of the GPS/INS/Odometer integrated navigation system are smaller than those of GPS/INS integrated navigation system especially, where GPS signal is absent.
  35. 35. Results 35 Real data test results: - Calais city, France - Test vehicle is equipped with a Novatel GPS receiver - Duration 5 minutes and 25 seconds - Top speed of 16.5 meters/second (about 60 km/hour). - GPS positioning is absent first has duration of 40 seconds and the second 31 seconds. Real trajectory of the vehice with long GPS
  36. 36. Results 36 Real data test results: Velocity error of the vehicle Position error of the vehicle error doesn’t exceed 1.5 meters for East and North components
  37. 37. Results 37 Real data test results: - The generated trajectory from GPS/INS/Odometer integrated navigation system.
  38. 38. Results 38 Comparison:  The table below presents the standard deviation of position error for both simulation data and real scenario during GPS outages, respectively.
  39. 39. Summary 39  This paper proposes a new method for GPS/INS/Odometer integration based on Kalman filter that minimizes the INS error and provides continuous estimation of vehicle position, velocity and attitude.  This combination permit to avoid disadvantages of a stand-alone sensor in order to establish long-term navigation in GPS denied environments.  In this work the odometer bridging performance for the Inertial Navigation System during GPS outages, and the results obtained from simulation and real tests confirm that using an additional aiding source, the odometer, leads to good estimation of vehicle dynamic characteristics.
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