Using distributed magnetometers to increase IMU-based velocity estimation into perturbed area

Proceedings of the 2011 American Control Conference

Boudot

Hillion

et al. 2011

Self Cite

Abstract-We address the problem of estimating the position of a rigid body moving indoors. Disturbances of the magnetic field observed in buildings are used to derive a reliable velocity estimate. The estimated velocity is expressed in the body reference frame, which imposes to simultaneously reconstruct the rotation of this frame with respect to an inertial frame of reference. For this, an inertial measurement unit (IMU) is used. To maximize the accuracy of the reconstructed motion, alignment and calibration of the inertial sensors have to be carefully treated, which minimizes projection errors. A first contribution of this paper is an alignment-calibration technique combining gyrometers and accelerometers to address the attitude estimation problem. A second contribution is an observer of the velocity, the convergence of which is proved. Finally, an experimental testbench is described and experimental results are provided.

Section: B Equations Of Motionmentioning

confidence: 99%

“…An other approach to improve the efficiency of inertial systems as been first proposed in [17], [16]. It relies on a set of magnetometers to monitor the magnetic field and its spatial derivatives.…”

Section: Introductionmentioning

confidence: 99%

Combining inertial measurements and distributed magnetometry for motion estimation

Proceedings of the 2011 American Control Conference

Boudot

Hillion

et al. 2011

Self Cite

“…The technique takes advantage of the unknown magnetic field disturbances usually observed indoor to reduce drift in velocities. This technique has been successfully applied for indoor laboratory experiments in [12]. Yet, down-scaling of the system (which is of paramount importance for embedded applications), and the need to substitute off-line computations and data treatments techniques with real-time computations has raised several important issues.…”

Section: Introductionmentioning

confidence: 99%

On-the-field calibration of an array of sensors

Proceedings of the 2010 American Control Conference

Vissière²,

Petit

2010

Abstract-We address the problem of the calibration of an array of sensors by investigating theoretically and experimentally the case of 2 three-axis sensors. Our focus is on magnetometers that can be used in a low-cost inertial navigation system. Usual errors (misalignments, non-orthogonality, scale factors, biases) are accounted for. The proposed calibration method requires no specific calibration hardware. Instead, we solely use the fact that, if the sensor is properly calibrated, the norm of the sensed field must remain constant irrespective of the sensors orientation. Several strategies of calibration for an array of sensors are described along with the impact of (unavoidable) field disturbances. Experiments conducted with a couple of magneto-resistive magnetometers and data fusion results illustrate the relevance of the approach.

“…According to Equation (9), to close the reconstructed trajectory, and thus correct the pitch angle discussed in Section II, one simply has to find an harmonization matrix that sends…”

Section: A Further Theoretical Investigationsmentioning

confidence: 99%

“…The attitude is classically estimated by use of inertial sensors (see [3], and the references therein) while the velocity estimate is provided through a set of spatially distributed magnetometers as proposed in [9]. Briefly, the magnetic field dynamics in the velocity frame writeṡ…”

Section: Experimental Validationmentioning

confidence: 99%

Harmonization of a multi-sensor navigation system

2011 International Conference on Indoor Positioning and Indoor Navigation

Petit

2011

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Abstract-We address the problem of harmonization of two subsystems used separately to measure a velocity in a body reference frame and an attitude with respect to an inertial frame. This general problem is of particular importance when the two subsystems are jointly used to obtain an estimate of the inertial velocity, and, then, the position through an integration procedure. Typical possible applications encompass pedestrian navigation, and, more generally, indoor navigation. The paper demonstrates how harmonization can be achieved thanks to an analysis of propagation of projection errors. In particular, trajectories known to be closed can be used to determine one dominant harmonization factor which is the pitch relative orientation angle between the two subsystems. Computations yielding the harmonization procedure are exposed in this article, and experimental data serve to illustrate the application of the proposed method.