Integration of GPS/INS/ODOMeter for continuous navigation even if GPS is not available.

1. GPS/INS/ODOMETER DATA
FUSION FOR VEHICLE
LOCALIZATION
IN GPS DENIED ENVIRONMENT
1

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. Outline
3
 Objectives
 Introduction
 Comparison with Previous Work
 Block diagram of Proposed Method
 INS Modeling
 Odometer Modeling
 System model
 Observation Model
 Results
 Summary

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. 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. 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. 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. 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. Comparison with Previous Work
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Previous Work
Nav1 Nav2
Nav1
Nav2
Present Work

10. Block diagram of Proposed Method
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11. INS Modeling
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 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. INS Modeling
12
The dynamic equations in the n-frame are given by following equations

13. INS Modeling
13

14. INS Modeling
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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. 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. 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.
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17. INS Modeling
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Where,
Re is the radius of the earth and g is the
gravity

18. INS Modeling
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Where,

19. INS Modeling
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20. INS Modeling
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Block diagram representation of a
strapdown INS.

21. Odometer Modeling
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Abuhadrous, 2005

22. Odometer Modeling
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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. Odometer Modeling
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 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. System model
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 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. System model
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 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. 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. System model
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are the Gauss-Markov process driving noises

28. System model
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Where,

29. System model
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 The discrete form of the system model can be written as:
Where ∆t is the sampling time.

30. Observation Model
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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. Observation Model
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Odometer measurements :
- integrated with the INS at the rate of 1 Hz

32. Results
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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. Results
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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. Results
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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. Results
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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. Results
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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. Results
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Real data test results:
- The generated trajectory from
GPS/INS/Odometer integrated navigation system.

38. Results
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Comparison:
 The table below presents the standard deviation of position error for both simulation data and real scenario during
GPS outages, respectively.

39. Summary
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 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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