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Attitude Fusion of Inertial and Magnetic Sensor under Different Magnetic Filed Distortions

Attitude Fusion of Inertial and Magnetic Sensor under Different Magnetic Filed Distortions By virtue of gravity measurement from a handheld inertial measurement unit (IMU) sensor, current indoor attitude estimation algorithms can provide accurate roll/pitch dimension angles. Acquisition of precise heading is limited by the absence of accurate magnetic reference. Consequently, initial stage magnetometer calibration is deployed to alleviate this bottleneck in attitude fusion. However, available algorithms tackle magnetic distortion based on time-invariant surroundings, casting the post-calibration magnetic data into unchanged ellipsoid centered in the calibration place. Consequently, inaccurate fusion results are formulated in a more common case of random walk in time-varying magnetic indoor environment. This article proposes a new fusion algorithm from various kinds of IMU sensors, namely gyroscope, accelerometer, and magnetometer. Compared to state-of-the-art attitude fusion approaches, this article addresses the indoor time-varying magnetic perturbation problem in a geometric view. We propose an extend Kalman filter--based algorithm based on this detailed geometric model to eliminate the position-dependent effect of a compass sensor. Experimental data demonstrate that, under different indoor magnetic distortion environments, our proposed attitude fusion algorithm has the maximum angle error of 2.02, outperforming 7.17 of a gradient-declining-based algorithm. Additionally, this attitude fusion result is constructed in a low-cost handheld arduino core--based IMU device, which can be widely applied to embedded systems. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Embedded Computing Systems (TECS) Association for Computing Machinery

Attitude Fusion of Inertial and Magnetic Sensor under Different Magnetic Filed Distortions

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References (50)

Publisher
Association for Computing Machinery
Copyright
Copyright © 2018 ACM
ISSN
1539-9087
eISSN
1558-3465
DOI
10.1145/3157668
Publisher site
See Article on Publisher Site

Abstract

By virtue of gravity measurement from a handheld inertial measurement unit (IMU) sensor, current indoor attitude estimation algorithms can provide accurate roll/pitch dimension angles. Acquisition of precise heading is limited by the absence of accurate magnetic reference. Consequently, initial stage magnetometer calibration is deployed to alleviate this bottleneck in attitude fusion. However, available algorithms tackle magnetic distortion based on time-invariant surroundings, casting the post-calibration magnetic data into unchanged ellipsoid centered in the calibration place. Consequently, inaccurate fusion results are formulated in a more common case of random walk in time-varying magnetic indoor environment. This article proposes a new fusion algorithm from various kinds of IMU sensors, namely gyroscope, accelerometer, and magnetometer. Compared to state-of-the-art attitude fusion approaches, this article addresses the indoor time-varying magnetic perturbation problem in a geometric view. We propose an extend Kalman filter--based algorithm based on this detailed geometric model to eliminate the position-dependent effect of a compass sensor. Experimental data demonstrate that, under different indoor magnetic distortion environments, our proposed attitude fusion algorithm has the maximum angle error of 2.02, outperforming 7.17 of a gradient-declining-based algorithm. Additionally, this attitude fusion result is constructed in a low-cost handheld arduino core--based IMU device, which can be widely applied to embedded systems.

Journal

ACM Transactions on Embedded Computing Systems (TECS) – Association for Computing Machinery

Published: Jan 30, 2018

Keywords: Embedded system

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