The Limits of In-Run Calibration of MEMS Inertial Sensors and Sensor Arrays

Martin, H. Newell; Groves, Paul D.; Newman, Mark

doi:10.1002/navi.135

Cited by 23 publications

(10 citation statements)

References 17 publications

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“…The work by [42,43] performs online calibration of all three tri-axial sensors in MIMU. There is however limitations to online approach, as highlighted by [5]. Also the quality and space coverage of the data dictates the calibration accuracy on unseen data [44,45].…”

Section: F In-situ Calibration Of Sensorsmentioning

confidence: 99%

“…The MEMS accelerometer, rate gyro and magnetometer are not only affected by systematic errors of bias, sensitivity, non-orthogonality and misalignment, but also undergo stochastic errors. The precise calibration of these multi-MIMU systems, remains a major challenge [5]. The computation of a state or latent features from sensor inputs often implies integration over time, like integration of rate gyro readings for orientation estimation or of accelerometer readings for navigation.…”

Section: Introductionmentioning

confidence: 99%

“…This does not require full space coverage like multi-position [17] or [18]. (5) To the best of our knowledge, our work is the first to learn the data-driven uncertainty of calibrated readings from multiple MIMUs using novel mutual kinematic constraints. We show that this uncertainty correlates well with the error both in static and dynamic conditions.…”

Section: Introductionmentioning

confidence: 99%

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Simultaneous End User Calibration of Multiple Magnetic Inertial Measurement Units With Associated Uncertainty

et al. 2021

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A fast method to simultaneously calibrate multiple MEMS Magnetic Inertial Measurement Units (MIMUs) accurately in the field is needed in many application areas. The MEMS MIMUs require calibration of systematic errors of bias, sensitivity, non-orthogonality and misalignment, which vary with temperature and use. Even after calibration, the sensors undergo stochastic errors in static and dynamic conditions and thus uncertainty of output must also be modeled. We propose a method for easy and fast calibration of multiple MIMUs together, while mounted on a single platform. The precise alignment of sensors is not assumed. Our method calibrates both fixed array of MEMS MIMUs or many independent MIMUs simultaneously using kinematic constraints. The novelty of our approach is that the uncertainty of sensors output is also learned as part of our model. Compared with existing state-of-art methods, our algorithm gives more consistent readings of all MIMUs and our framework also predicts the associated uncertainty of the sensor output. The uncertainty prediction of individual sensors is particularly helpful in the sensor fusion.

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Section: F In-situ Calibration Of Sensorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Simultaneous End User Calibration of Multiple Magnetic Inertial Measurement Units With Associated Uncertainty

et al. 2021

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“…Most of the works [13,14,15,19,21,30] cited above utilized high-end commercial consumer grade MEMS sensors with costs in the order of thousands of US dollars [31], but it is not practical to equip pedestrians with expensive sensors. This type of sensor is designed to provide accurate inertial-based motion tracking [25] with orientation accuracy of 1.5 degrees RMS or less [32].…”

Section: Introductionmentioning

confidence: 99%

Using Step Size and Lower Limb Segment Orientation from Multiple Low-Cost Wearable Inertial/Magnetic Sensors for Pedestrian Navigation

Tjhai¹,

O’Keefe²

2019

Sensors

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This paper demonstrates the use of multiple low-cost inertial/magnetic sensors as a pedestrian navigation system for indoor positioning. This research looks at the problem of pedestrian navigation in a practical manner by investigating dead-reckoning methods using low-cost sensors. This work uses the estimated sensor orientation angles to compute the step size from the kinematics of a skeletal model. The orientations of limbs are represented by the tilt angles estimated from the inertial measurements, especially the pitch angle. In addition, different step size estimation methods are compared. A sensor data logging system is developed in order to record all motion data from every limb segment using a single platform and similar types of sensors. A skeletal model of five segments is chosen to model the forward kinematics of the lower limbs. A treadmill walk experiment with an optical motion capture system is conducted for algorithm evaluation. The mean error of the estimated orientation angles of the limbs is less than 6 degrees. The results show that the step length mean error is 3.2 cm, the left stride length mean error is 12.5 cm, and the right stride length mean error is 9 cm. The expected positioning error is less than 5% of the total distance travelled.

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“…Rather, they measure its linear acceleration, angular velocity, and the magnetic field surrounding the sensor. Angular velocity can be numerically integrated to obtain rotation, but MIMUs designed for human motion analysis are based on low-cost microelectromechanical technology that also suffers from substantial noise and bias over the measurement [ 5 ]. This noise and bias particularly affects the angular velocity measure, [ 6 ] such that the estimation of the orientation by numerical integration leads to a well-known error: orientation drift [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Identification of Noise Covariance Matrices to Improve Orientation Estimation by Kalman Filter

Nez

Fradet

Marin

et al. 2018

Sensors

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Magneto-inertial measurement units (MIMUs) are a promising way to perform human motion analysis outside the laboratory. To do so, in the literature, orientation provided by an MIMU is used to deduce body segment orientation. This is generally achieved by means of a Kalman filter that fuses acceleration, angular velocity, and magnetic field measures. A critical point when implementing a Kalman filter is the initialization of the covariance matrices that characterize mismodelling and input error from noisy sensors. The present study proposes a methodology to identify the initial values of these covariance matrices that optimize orientation estimation in the context of human motion analysis. The approach used was to apply motion to the sensor manually, and to compare the orientation obtained via the Kalman filter to a measurement from an optoelectronic system acting as a reference. Testing different sets of values for each parameter of the covariance matrices, and comparing each MIMU measurement with the reference measurement, enabled identification of the most effective values. Moreover, with these optimized initial covariance matrices, the orientation estimation was greatly improved. The method, as presented here, provides a unique solution to the problem of identifying the optimal covariance matrices values for Kalman filtering. However, the methodology should be improved in order to reduce the duration of the whole process.

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The Limits of In-Run Calibration of MEMS Inertial Sensors and Sensor Arrays

Cited by 23 publications

References 17 publications

Simultaneous End User Calibration of Multiple Magnetic Inertial Measurement Units With Associated Uncertainty

Simultaneous End User Calibration of Multiple Magnetic Inertial Measurement Units With Associated Uncertainty

Using Step Size and Lower Limb Segment Orientation from Multiple Low-Cost Wearable Inertial/Magnetic Sensors for Pedestrian Navigation

Identification of Noise Covariance Matrices to Improve Orientation Estimation by Kalman Filter

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