Improving magneto-inertial attitude and position estimation by means of a magnetic heading observer

Abstract

Abstract-This paper studies heading estimation jointly with the attitude and position estimation of a rigid body equipped with inertial and magnetic sensors in indoor environment. In contrast with other indoor dead-reckoning approaches, no assumption is made about the nature of the movement or environment layout. Based on a previous paper, an Extended Kalman Filter is designed, which includes inertial sensor biases and magnetic disturbances. A heuristic model of the dynamic of magnetic heading disturbances is then described and added to the observer. The latter is then evaluated in terms of position and heading error on experimental data, showing that in spite of high levels of disturbances, the magnetic field alone can be used to compute heading.

Calibration of a magnetometer array using motion capture equipment

“…As in , let be a navigation frame, and a frame of reference moving with the rigid body. Coordinates of vectors in (resp.…”

“…We use an Optitrack™ motion capture equipment, see Figure 2, providing trajectory data at ∼ 240Hz, to track the movement of a sensor board carrying magnetometers and micro-electro-mechanical inertial sensors. The sensor board is the same that was used in [17,18,20,21]. All sensors are sampled at f ≈ 325 Hz.…”

“…This paper presents a calibration technique for magnetometer arrays solving a problem inverse to magneto‐inertial dead‐reckoning. Whereas the latter, applicable for an indoor stationary (inhomogeneous) magnetic field , allows us to obtain positions and attitudes as a function of the calibration parameters (and the raw magnetic data), the present method builds on raw magnetic data paired with motion capture data, i.e. position and attitude of the array of the moving sensor device, in order to estimate calibration parameters as well as the magnetic field.…”

Improving Inertial Velocity Estimation Through Magnetic Field Gradient-based Extended Kalman Filter

“…Therefore rich information lies in those disturbances. Based on these phenomena, magneto-inertial approaches are proposed recently in [11], [12], [13], [14] with different dynamic models. They preserve the main advantages of purely inertial technology: no prior mapping and signal are required.…”

A hybrid observer for localization of mobile vehicles with asynchronous measurements

“…The above cited problems are not new in literature and they have been faced both in a stochastic framework and in a deterministic framework. With regard to the stochastic framework, Kalman filters have been developed, for example Extended Kalman Filters (EKFs) have been proposed in many works [12][13][14][15][16][17][18][19][20] in order to deal with intermittent measurements, displaying good behaviors also in presence of measurement data missing. In [21] and [22] adaptive Kalman filters have been proposed, where the covariance matrices are online updated according with the low rate of the positioning system.…”