Iterative calibration method for inertial and magnetic sensors

Abstract

Abstract-We address the problem of three-axis sensor calibration. Our focus is on magnetometers. Usual errors (misalignment, non-orthogonality, scale factors, biases) are accounted for. We consider a method where no specific calibration hardware is required. We solely use the fact that the norm of the sensed field must remain constant irrespective of the sensors orientation. The proposed algorithm is iterative. Its convergence is studied. Experiments conducted with MEMS sensors (magnetometers) stress the relevance of the approach.

Uav-Based Acquisition of 3d Point Cloud – A Comparison of a Low-Cost Laser Scanner and SFM-Tools

“…Due to this fact, an additional improvement of the used navigation solution may be obtained from sensors like a navigation camera in a visual SLAM approach similar to (Georgiev and Allen, 2004). A further aspect for enhancement would be an appropriate magnetometer calibration as described in (Gebre-Egziabher, 2007, Dorveaux et al, 2009, Metni et al, 2006 or improved protection against signal interfering radiation through EMI shields. Figure 6d discloses a major problem caused by direct dependencies of navigation solution, in this case the IMU angles.…”

On-the-field calibration of an array of sensors

“…This has raised a huge interest in developing "simple but effective" calibration procedures that do not require a high degree of expertise nor an expensive hardware to be put into practice. Lately, some procedures and algorithms have been proposed for accelerometers (see [11], [6]) and magnetometers (see [8], [7], [3]) calibration. They all rely on the fact that the force field under consideration (respectively, the gravitational field and the Earth magnetic field) corresponds to a vector having, in theory, a constant and known norm.…”

“…Several choices are possible and all yield some distortion in the cost function. The iterative algorithm proposed in [3] gets rid of this drawback by solving a sequence of least square problems in which the input data are iteratively calibrated. We now briefly recall this algorithm.…”

“…We now briefly recall this algorithm. Details on its properties (convergence and efficiency) can be found in [3].…”

“…By rotating the sensor in every possible direction, misalignments and biases can be identified. In [3] and [8], such methods were proposed. As all the "tablefree" calibration methods, this method has several practical advantages.…”