In-Field Gyroscope Autocalibration with Iterative Attitude Estimation
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2021
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| _version_ | 1866910565523259392 |
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| author | Wang, Li Duffield, Rob Fox, Deborah Hammond, Athena Zhang, Andrew J. Zheng, Wei Xing Su, Steven W. |
| author_facet | Wang, Li Duffield, Rob Fox, Deborah Hammond, Athena Zhang, Andrew J. Zheng, Wei Xing Su, Steven W. |
| contents | This paper presents an efficient in-field calibration method tailored for low-cost triaxial MEMS gyroscopes often used in healthcare applications. Traditional calibration techniques are challenging to implement in clinical settings due to the unavailability of high-precision equipment. Unlike the auto-calibration approaches used for triaxial MEMS accelerometers, which rely on local gravity, gyroscopes lack a reliable reference since the Earth's self-rotation speed is insufficient for accurate calibration. To address this limitation, we propose a novel method that uses manual rotation of the MEMS gyroscope to a specific angle (360°) as the calibration reference. This approach iteratively estimates the sensor's attitude without requiring any external equipment. Numerical simulations and empirical tests validate that the calibration error is low and that parameter estimation is unbiased. The method can be implemented in real-time on a low-energy microcontroller and completed in under 30 seconds. Comparative results demonstrate that the proposed technique outperforms existing state-of-the-art methods, achieving scale factor and bias errors of less than $2.5\times10^{-2}$ for LSM9DS1 and less than $1\times10^{-2}$ for ICM20948. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2103_11097 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | In-Field Gyroscope Autocalibration with Iterative Attitude Estimation Wang, Li Duffield, Rob Fox, Deborah Hammond, Athena Zhang, Andrew J. Zheng, Wei Xing Su, Steven W. Systems and Control This paper presents an efficient in-field calibration method tailored for low-cost triaxial MEMS gyroscopes often used in healthcare applications. Traditional calibration techniques are challenging to implement in clinical settings due to the unavailability of high-precision equipment. Unlike the auto-calibration approaches used for triaxial MEMS accelerometers, which rely on local gravity, gyroscopes lack a reliable reference since the Earth's self-rotation speed is insufficient for accurate calibration. To address this limitation, we propose a novel method that uses manual rotation of the MEMS gyroscope to a specific angle (360°) as the calibration reference. This approach iteratively estimates the sensor's attitude without requiring any external equipment. Numerical simulations and empirical tests validate that the calibration error is low and that parameter estimation is unbiased. The method can be implemented in real-time on a low-energy microcontroller and completed in under 30 seconds. Comparative results demonstrate that the proposed technique outperforms existing state-of-the-art methods, achieving scale factor and bias errors of less than $2.5\times10^{-2}$ for LSM9DS1 and less than $1\times10^{-2}$ for ICM20948. |
| title | In-Field Gyroscope Autocalibration with Iterative Attitude Estimation |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2103.11097 |