Details
| Original language | English |
|---|---|
| Article number | 7 |
| Pages (from-to) | 1-30 |
| Number of pages | 30 |
| Journal | Sensors (Switzerland) |
| Volume | 21 |
| Issue number | 1 |
| Publication status | Published - 1 Jan 2021 |
| Externally published | Yes |
Abstract
Multi-modal sensor fusion has become ubiquitous in the field of vehicle motion estimation. Achieving a consistent sensor fusion in such a set-up demands the precise knowledge of the mis-alignments between the coordinate systems in which the different information sources are expressed. In ego-motion estimation, even sub-degree misalignment errors lead to serious performance degrada-tion. The present work addresses the extrinsic calibration of a land vehicle equipped with standard production car sensors and an automotive-grade inertial measurement unit (IMU). Specifically, the article presents a method for the estimation of the misalignment between the IMU and vehicle coordinate systems, while considering the IMU biases. The estimation problem is treated as a joint state and parameter estimation problem, and solved using an adaptive estimator that relies on the IMU measurements, a dynamic single-track model as well as the suspension and odometry systems. Additionally, we show that the validity of the misalignment estimates can be assessed by identifying the misalignment between a high-precision INS/GNSS and the IMU and vehicle coordinate systems. The effectiveness of the proposed calibration procedure is demonstrated using real sensor data. The results show that estimation accuracies below 0.1 degrees can be achieved in spite of moderate variations in the manoeuvre execution.
Keywords
- Automotive industry, Autonomous driving, Extrinsic calibration, Inertial sensors, Kalman filter, Motion estimation, Nonlinear systems, Odometry, Simultaneous state and parameter estimation, Systems and control engineering
ASJC Scopus subject areas
- Chemistry(all)
- Analytical Chemistry
- Computer Science(all)
- Information Systems
- Physics and Astronomy(all)
- Atomic and Molecular Physics, and Optics
- Biochemistry, Genetics and Molecular Biology(all)
- Biochemistry
- Physics and Astronomy(all)
- Instrumentation
- Engineering(all)
- Electrical and Electronic Engineering
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In: Sensors (Switzerland), Vol. 21, No. 1, 7, 01.01.2021, p. 1-30.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - A novel IMU extrinsic calibration method for mass production land vehicles
AU - Marco, Vicent Rodrigo
AU - Kalkkuhl, Jens
AU - Raisch, Jörg
AU - Seel, Thomas
N1 - Funding Information: We acknowledge the support by the German Research Foundation and the Open Access Publication Fund of TU Berlin.
PY - 2021/1/1
Y1 - 2021/1/1
N2 - Multi-modal sensor fusion has become ubiquitous in the field of vehicle motion estimation. Achieving a consistent sensor fusion in such a set-up demands the precise knowledge of the mis-alignments between the coordinate systems in which the different information sources are expressed. In ego-motion estimation, even sub-degree misalignment errors lead to serious performance degrada-tion. The present work addresses the extrinsic calibration of a land vehicle equipped with standard production car sensors and an automotive-grade inertial measurement unit (IMU). Specifically, the article presents a method for the estimation of the misalignment between the IMU and vehicle coordinate systems, while considering the IMU biases. The estimation problem is treated as a joint state and parameter estimation problem, and solved using an adaptive estimator that relies on the IMU measurements, a dynamic single-track model as well as the suspension and odometry systems. Additionally, we show that the validity of the misalignment estimates can be assessed by identifying the misalignment between a high-precision INS/GNSS and the IMU and vehicle coordinate systems. The effectiveness of the proposed calibration procedure is demonstrated using real sensor data. The results show that estimation accuracies below 0.1 degrees can be achieved in spite of moderate variations in the manoeuvre execution.
AB - Multi-modal sensor fusion has become ubiquitous in the field of vehicle motion estimation. Achieving a consistent sensor fusion in such a set-up demands the precise knowledge of the mis-alignments between the coordinate systems in which the different information sources are expressed. In ego-motion estimation, even sub-degree misalignment errors lead to serious performance degrada-tion. The present work addresses the extrinsic calibration of a land vehicle equipped with standard production car sensors and an automotive-grade inertial measurement unit (IMU). Specifically, the article presents a method for the estimation of the misalignment between the IMU and vehicle coordinate systems, while considering the IMU biases. The estimation problem is treated as a joint state and parameter estimation problem, and solved using an adaptive estimator that relies on the IMU measurements, a dynamic single-track model as well as the suspension and odometry systems. Additionally, we show that the validity of the misalignment estimates can be assessed by identifying the misalignment between a high-precision INS/GNSS and the IMU and vehicle coordinate systems. The effectiveness of the proposed calibration procedure is demonstrated using real sensor data. The results show that estimation accuracies below 0.1 degrees can be achieved in spite of moderate variations in the manoeuvre execution.
KW - Automotive industry
KW - Autonomous driving
KW - Extrinsic calibration
KW - Inertial sensors
KW - Kalman filter
KW - Motion estimation
KW - Nonlinear systems
KW - Odometry
KW - Simultaneous state and parameter estimation
KW - Systems and control engineering
UR - http://www.scopus.com/inward/record.url?scp=85098512192&partnerID=8YFLogxK
U2 - 10.3390/s21010007
DO - 10.3390/s21010007
M3 - Article
AN - SCOPUS:85098512192
VL - 21
SP - 1
EP - 30
JO - Sensors (Switzerland)
JF - Sensors (Switzerland)
SN - 1424-8220
IS - 1
M1 - 7
ER -