AN IMU/MAGNETOMETER/GPS-BASED LOCALIZATION SYSTEM USING CORRELATED KALMAN FILTERING

Abstract— This paper presents a new algorithm for data integration from IMU, GPS receiver and 3-axismagnetometer in order to provide complete 3D localization estimates. It has been shown that, for the proposeddata fusin strategy, measurement and process noises are correlated. Thus, this algorithm uses the CorrelatedExtended Kalman Filter, resulting in better statistical properties of the estimates when compared to classicalExtended Kalman Filter. The results obtained with both ﬁlters are compared experimentaly in a real localizationsystem prototype.Keywords— Multi-sensor data fusion, correlated Kalman ﬁltering, IMU, magnetometer, GPS. 1 IntroductionReal-time and accurate 3D localization data iscritical for aerial robots, since those vehicles nor-mally present high instability when compared torobots that navigate on the ground or on the wa-ter. The employment of systems used in aeronau-tics, however, is often prohibitive in terms of size,weight, power consumption and cost, particularlysmall and short range systems. Hence, the devel-opment of systems based on on low cost micro-electro-mechanical systems sensors is of great in-terest.In order to provide the ability for the aircraftto navigate autonomously outdoors and on non-controlledenvironments, in thisprojectthefollow-ing set of sensors has been chosen: accelerometers,gyrometers, magnetometers and Global Position-ing System (GPS) receiver. Inertial MeasurementUnits (IMUs), composed by sets of accelerometersand gyrometers, provide high bandwidth 3D local-ization information, but with unbounded errors inlong term operation. For this reason, often othersensors, like a GPS receiver, are used to correctthe estimates provided by the IMU.The literature regarding IMU/GPS integra-tion is extensive (Grewal et al., 2001), speciallyusing the Extended Kalman Filter (EKF). Na-tional research has also been conducted about thetopic, for instance by Hemerly and Schad (2004),in which an EKF is applied not only to integratedata from IMU and GPS, but also to perform on-line sensor bias calibration. In addition, otherfusion algorithms have already been applied, likethe Sigma-Point Kalman Filter (SPKF) in van derMerwe and Wan (2004) and the Particle Filter(PF) in Carvalho et al. (1997). On the other hand, the amount of works re-lated to systems composed by an IMU, GPS and3-axis magnetometer is limited. Often IMU andmagnetometers are used to address the problem ofattitude determination alone, like in Marins et al.(2001) and in Bo and Borges (2007b), in whichquaternion-based ﬁlters were used to estimate theattitude. In Bo and Borges (2007a), the devel-opment of a 3D localization system based on thisset of sensors was described. The system used anEKF as the sensor fusion algorithm and was ableto perform sensor online calibration.One of the issues regarding the use of a 3-axismagnetometer in a IMU/GPS localization systemis that, while the GPS receiver provides absoluteposition and velocity measurements in Earth co-ordinates, the magnetometer is not able to pro-vide complete attitude corrections, since rotationsabout the local magnetic ﬁeld are not detected.To overcome this problem, frequently an estimateof the local gravitational ﬁeld provided by the ac-celerometers is additionally applied, in order toprovide complete attitude estimation. However,within this approach, the prediction and correc-tion noises become correlated, a condition whichis not considered in most applied algorithms.The main contribution of this paper with re-spect to our previous work is a new algorithm thatintegrates data from IMU, GPS receiver and 3-axis magnetometer in order to provide complete3D localization estimates. This algorithm uses theCorrelated Extended Kalman Filter (CEKF), re-sulting in better statistical properties of the esti-mates. The results obtained with that algorithmare compared experimentaly with the output pro-vided by an EKF.