Autocalibration of Triaxial MEMS Accelerometers With Automatic Sensor Model Selection

Frosio, Iuri; Pedersini, F.; Borghese, N. Alberto

doi:10.1109/jsen.2012.2182991

Cited by 54 publications

(41 citation statements)

References 18 publications

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“…pairs of lines) to reliably estimate the camera internal parameters after zooming. Recalibration is therefore drastically simplified, as straight lines are commonly present in the scenes [13,26] and they can be automatically identified and accurately fitted with standard computer vision methods [13].…”

Section: Discussionmentioning

confidence: 99%

“…Indeed, the camera orientation R 1 does not change when the camera zooms, but the position of the optical center moves forward and backward when the barrel of the lens rotates during zooming. Similarly to [17], we have used here the exif data to estimate the shift δ with a typical approximation (bounded by the exif data discretization step) which is in the order of ±1mm [26]. Exif data are generally available for any picture acquired by digital cameras and/or mobile phone; for cameras in an industrial or robotic context, these data can generally be retrieved from the API provided with the camera SDK or general purpose software for camera controlling [18].…”

Section: Discussionmentioning

confidence: 99%

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Camera re-calibration after zooming based on sets of conics

2015

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We describe a method to compute the internal parameters (focal and principal point) of a camera with known position and orientation, based on the observation of two or more conics on a known plane. The conics can even be degenerate (e.g., pairs of lines). The proposed method can be used to re-estimate the internal parameters of a fully calibrated camera after zooming to a new, unknown, focal length. It also allows to estimate the internal parameters when a second, fully calibrated camera observes the same conics. The parameters estimated through the proposed method are coherent with the output of more traditional procedures, that require a higher number of calibration images. A deep analysis of the geometrical configurations that influence the proposed method is also reported.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Camera re-calibration after zooming based on sets of conics

2015

Self Cite

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“…methods estimate three gains and three bias parameters [3,4]. This is sufficient if the accelerometer axes can be assumed to be perfectly orthogonal, and if the cross-axis interference caused by electric coupling in the electronics is negligible [5]. For lower quality sensors these assumptions are typically not valid, and as a result of this, up to three additional parameters have to be introduced and estimated to compensate for these errors [6].…”

Section: Introductionmentioning

confidence: 99%

Accelerometer calibration using sensor fusion with a gyroscope

Olsson

Kok

Halvorsen

et al. 2016

2016 IEEE Statistical Signal Processing Workshop (SSP)

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In this paper, a calibration method for a triaxial accelerometer using a triaxial gyroscope is presented. The method uses a sensor fusion approach, combining the information from the accelerometers and gyroscopes to find an optimal calibration using Maximum likelihood. The method has been tested by using real sensors in smartphones to perform orientation estimation and verified through Monte Carlo simulations. In both cases, the method is shown to provide a proper calibration, reducing the effect of sensor errors and improving orientation estimates.

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“…prototype sensor, like the one described in [1]),  values of important metrological parameters of a sensor are over-or under-estimated in the related catalog,  a sensor must be calibrated by the user (there exist also some calibration methods with no test rig necessary, described e.g. in [2,3], however their application results in a decrease of the accuracy of the sensor calibrated that way),  results of ageing of a sensor must be determined.…”

Section: Introductionmentioning

confidence: 99%

Dual-Axis Test Rig for Mems Tilt Sensors

Łuczak¹

2014

Metrology and Measurement Systems

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The paper addresses the problem of experimental studies of miniature tilt sensors based on low-range accelerometers belonging to Microelectromechanical Systems (MEMS). A custom computer controlled test rig is proposed, whose kinematics allows an arbitrary tilt angle to be applied (i.e. its two components: pitch and roll over the full angular range). The related geometrical relationships are presented along with the respective uncertainties resulting from their application. Metrological features of the test rig are carefully evaluated and briefly discussed. Accuracy of the test rig is expressed in terms of the respective uncertainties, as recommended by ISO; its scope of application as well as the related limitations are indicated. Even though the test rig is mostly composed of standard devices, like rotation stages and incremental angle encoder, its performance can be compared with specialized certified machines that are very expensive. Exemplary results of experimental studies of MEMS accelerometers realized by means of the test rig are presented and briefly discussed. Few ways of improving performance of the test rig are proposed.

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Autocalibration of Triaxial MEMS Accelerometers With Automatic Sensor Model Selection

Cited by 54 publications

References 18 publications

Camera re-calibration after zooming based on sets of conics

Camera re-calibration after zooming based on sets of conics

Accelerometer calibration using sensor fusion with a gyroscope

Dual-Axis Test Rig for Mems Tilt Sensors

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