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IMPLEMENTATION OF A GPS/INS/ODOMETER NAVIGATION SYSTEM
Elder M. Hemerly
Technological Institute of Aeronautics - Electronics Division, Systems and Control Department
Pça. Mal. Eduardo Gomes, 50 - Vila das Acácias 12228-900 São José dos Campos/ SP - Brazil
hemerly@ita.br
Valter R. Schad
NAVCON- Navegação e Controle Ind. & Com. Ltda.
Rua Marabá, 35 – Parque Industrial 12235-780 São José dos Campos /SP - Brazil
valterschad@navcon.com.br
Abstract. The complementary properties of the GPS and the INS have motivated several works dealing with their
fusion by means of a Kalman Filter. However, if the GPS signal is unavailable this fusion is actually equivalent to an
unassisted INS. This occurs, for instance, when the vehicle enters a tunnel. An efficient and simple solution in these
cases is to implement also an odometer/INS integration system. This multi-sensor based navigation system is
implemented in this paper, and the key issues, namely data synchronization, multirate operation and GPS antenna
lever arm compensation, are properly dealt with. The basic error modeling in NED is described and the integration
Kalman Filter is discussed. Experimental results show the effectiveness of the GPS/INS/odometer integrated system. As
far as the authors are aware, this is the first reported experimental result of its kind in Brazil.
Keywords: GPS, INS, Odometer, Integrated Navigation System, Kalman Filter.
1. INTRODUCTION
The use of GPS (Global Positioning System) for assisting inertial navigation is a well established approach for
implementing navigation systems, due to its good characteristics: global coverage, fast acquisition, good accuracy and
low price. These characteristics nicely complement those exhibited by INS: inexistence of signal transmission, thence
no possibility of jamming or signal loss, and availability at high frequency. For general details on the GPS principles,
see Farrell and Barth (1999). Experimental results for the INS/GPS fusion, by means of the Kalman filter, can be found
in Ohlmeyer et al. (1999); Faruqi (2000); Salychev (2000) and Walchko (2003). See also Hemerly and Schad (2004) for
simulation results, and Hemerly and Schad (2005) for experimental ones.
In some applications, however, the GPS signal can be lost, as for instance when a vehicle enters a tunnel, and the
navigation solely based on INS can produce large errors in position and attitude. One possible solution is to employ
another sensor for assisting the INS navigation. In the applications envisaged in this work, the best option is to employ
an odometer: it has low cost and is accurate.
The use of odometer is particularly recommended when a low cost and precise vehicle localization system has to be
implemented and there is the risk of GPS coverage failure, which is prone to happen when the vehicle enters a tunnel or
cross deep valleys. In Abuhadrous et al. (2003) a multi-sensor data fusion system for land vehicle localization is
implemented. The 3 main sensors used are IMU, GPS and odometer. A 15 state Kalman Filter is employed, but there is
no odometer calibration, what is a too optimistic approach since in practice odometers are expected to exhibit errors,
mainly in their scale factors. In Ernest et al. (2004) a train locator using inertial sensors and odometer is proposed.
However, the error dynamics for use in the Kalman Filter is a crude cinematic model, which is bound to display error if
the vehicle undergoes fast dynamic. Moreover, no odometer calibration is performed in real time. A much more
efficient procedure for performing GPS/INS/Odometer integration is proposed by Seo et al. (2006): it includes lever
arm compensation for GPS antenna and odometer scale factor estimation in real time. The Kalman Filter output
equation is modified properly in order to account for the lever arm and one additional state, representing the odometer
scale factor, is included in the state equation used by the filter.
A GPS/INS/Odometer system for navigation is implemented and tested with real data in present work. It differs
from Seo et al. (2006) in the sense that the lever arm correction is performed by changing the output signal which drives
the filter, and not the output matrix, since this is a simpler approach. Moreover, the odometer bias is also estimated in
real time, so as to cope with eventual time variation in this bias. In the experimental results, the GPS is sampled at 10Hz
and the IMU and odometer are sampled at 100 Hz. During the experiment the vehicle enters a tunnel, and the GPS
signal is thereby lost.
This paper is organized as follows: in section 2 the dynamic equation for the error propagation, necessary for the
Kalman Filter implementation in the NED frame, is established. Details concerning the Kalman filter for implementing
the GPS/INS/Odometer fusion via the tight approach are also presented in section 2. Experimental results are presented
in section 3, which also includes a discussion of the main results obtained. The conclusions are then presented in section
4.
ABCM Symposium Series in Mechatronics - Vol. 3 - pp.519-524
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