Details
| Original language | English |
|---|---|
| Pages (from-to) | 187-204 |
| Number of pages | 18 |
| Journal | Information Fusion |
| Volume | 91 |
| Early online date | 19 Oct 2022 |
| Publication status | Published - Mar 2023 |
| Externally published | Yes |
Abstract
The miniaturization of MEMS-based inertial measurement units (IMUs) facilitates their widespread use in a growing number of application domains. The fundamental sensor fusion task of orientation estimation is a prerequisite for most further data processing steps in inertial motion tracking, such as position and velocity estimation, joint angle estimation, and 3D visualization. Errors in the estimated orientations severely affect all further processing steps. Recent systematic comparisons of existing algorithms show that out-of-the-box accuracy is often low and that application-specific tuning is required to obtain high accuracy. In the present work, we propose and extensively evaluate a quaternion-based orientation estimation algorithm that is based on a novel approach of filtering the acceleration measurements in an almost-inertial frame and that includes extensions for gyroscope bias estimation and magnetic disturbance rejection, as well as a variant for offline data processing. In contrast to all existing work, we perform an extensive evaluation, using a large collection of publicly available datasets and eight literature methods for comparison. The proposed method consistently outperforms all eight literature methods and achieves an average RMSE of 2.9°, while the errors obtained with literature methods range from 5.3° to 16.7°. This improved accuracy with respect to the state of the art is observed not only in average but also for each of several different motion characteristics, as well as for gyroscope bias estimation. Since the evaluation was performed with one single fixed parametrization across a very diverse dataset collection, we conclude that the proposed method provides unprecedented out-of-the-box performance for a broad range of motions, sensor hardware, and environmental conditions. This gain in orientation estimation accuracy is expected to advance the field of IMU-based motion analysis and provide performance benefits in numerous applications. The provided open-source implementation makes it easy to employ the proposed method.
Keywords
- AHRS, Attitude estimation, Gyroscope bias estimation, IMU, Inertial measurement unit, Inertial sensor, Magnetic disturbances, Orientation estimation, Sensor fusion
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Computer Science(all)
- Signal Processing
- Computer Science(all)
- Information Systems
- Computer Science(all)
- Hardware and Architecture
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In: Information Fusion, Vol. 91, 03.2023, p. 187-204.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - VQF
T2 - Highly accurate IMU orientation estimation with bias estimation and magnetic disturbance rejection
AU - Laidig, Daniel
AU - Seel, Thomas
PY - 2023/3
Y1 - 2023/3
N2 - The miniaturization of MEMS-based inertial measurement units (IMUs) facilitates their widespread use in a growing number of application domains. The fundamental sensor fusion task of orientation estimation is a prerequisite for most further data processing steps in inertial motion tracking, such as position and velocity estimation, joint angle estimation, and 3D visualization. Errors in the estimated orientations severely affect all further processing steps. Recent systematic comparisons of existing algorithms show that out-of-the-box accuracy is often low and that application-specific tuning is required to obtain high accuracy. In the present work, we propose and extensively evaluate a quaternion-based orientation estimation algorithm that is based on a novel approach of filtering the acceleration measurements in an almost-inertial frame and that includes extensions for gyroscope bias estimation and magnetic disturbance rejection, as well as a variant for offline data processing. In contrast to all existing work, we perform an extensive evaluation, using a large collection of publicly available datasets and eight literature methods for comparison. The proposed method consistently outperforms all eight literature methods and achieves an average RMSE of 2.9°, while the errors obtained with literature methods range from 5.3° to 16.7°. This improved accuracy with respect to the state of the art is observed not only in average but also for each of several different motion characteristics, as well as for gyroscope bias estimation. Since the evaluation was performed with one single fixed parametrization across a very diverse dataset collection, we conclude that the proposed method provides unprecedented out-of-the-box performance for a broad range of motions, sensor hardware, and environmental conditions. This gain in orientation estimation accuracy is expected to advance the field of IMU-based motion analysis and provide performance benefits in numerous applications. The provided open-source implementation makes it easy to employ the proposed method.
AB - The miniaturization of MEMS-based inertial measurement units (IMUs) facilitates their widespread use in a growing number of application domains. The fundamental sensor fusion task of orientation estimation is a prerequisite for most further data processing steps in inertial motion tracking, such as position and velocity estimation, joint angle estimation, and 3D visualization. Errors in the estimated orientations severely affect all further processing steps. Recent systematic comparisons of existing algorithms show that out-of-the-box accuracy is often low and that application-specific tuning is required to obtain high accuracy. In the present work, we propose and extensively evaluate a quaternion-based orientation estimation algorithm that is based on a novel approach of filtering the acceleration measurements in an almost-inertial frame and that includes extensions for gyroscope bias estimation and magnetic disturbance rejection, as well as a variant for offline data processing. In contrast to all existing work, we perform an extensive evaluation, using a large collection of publicly available datasets and eight literature methods for comparison. The proposed method consistently outperforms all eight literature methods and achieves an average RMSE of 2.9°, while the errors obtained with literature methods range from 5.3° to 16.7°. This improved accuracy with respect to the state of the art is observed not only in average but also for each of several different motion characteristics, as well as for gyroscope bias estimation. Since the evaluation was performed with one single fixed parametrization across a very diverse dataset collection, we conclude that the proposed method provides unprecedented out-of-the-box performance for a broad range of motions, sensor hardware, and environmental conditions. This gain in orientation estimation accuracy is expected to advance the field of IMU-based motion analysis and provide performance benefits in numerous applications. The provided open-source implementation makes it easy to employ the proposed method.
KW - AHRS
KW - Attitude estimation
KW - Gyroscope bias estimation
KW - IMU
KW - Inertial measurement unit
KW - Inertial sensor
KW - Magnetic disturbances
KW - Orientation estimation
KW - Sensor fusion
UR - http://www.scopus.com/inward/record.url?scp=85140458893&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2203.17024
DO - 10.48550/arXiv.2203.17024
M3 - Article
AN - SCOPUS:85140458893
VL - 91
SP - 187
EP - 204
JO - Information Fusion
JF - Information Fusion
SN - 1566-2535
ER -