Accelerometer calibration using sensor fusion with a gyroscope

Olsson, Fredrik; Kok, Manon; Halvorsen, Kjartan; Schön, Thomas B.

doi:10.1109/ssp.2016.7551836

Cited by 20 publications

(14 citation statements)

References 12 publications

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“…Filter type Assumption / limitation [15] Gravity and magnetic north Simple algorithm to reduce computational complexity at the cost of low accuracy [16] Opportunistic replacement Require nearby magnetic field to be stable; require frequent pause to reset the system with gravity [21] KF Require velocity readings as input [26,27] KF Rely on stable magnetic field or other sensors to get inclination angles [28] KF, gradient descent Require stable nearby magnetic field; assume initial orientation is known [29] KF Need redundant IMU sensors [30,31] KF Require stable nearby magnetic field; devices are stationary or in low acceleration consitions [32] KF Rotational motion of devices is slow [33] KF Estimate heading direction rather than 3D orientation [11,34] KF [34] , CF [11] Require stable nearby magnetic field [35] EKF Assume gyroscope's bias is constant; require stable nearby magnetic field [36,37] EKF Assume the device is stationary [38] EKF Need stationary condition to do initialization; require stable nearby magnetic field [39] UKF Require devices to be stationary or move slowly [40] UKF Require GPS as reference [41][42][43][44][45][46] EKF [41][42][43]46] , UKF [44] , CF [45,46] Require magnetic measurement to be accurate and the device to move slowly so that the gravity can be extracted [47,48] CF [47] , gradient descent [48] Requir...…”

Section: Related Workmentioning

confidence: 99%

Inertial motion tracking on mobile and wearable devices: Recent advancements and challenges

Song

Cao

et al. 2021

Tsinghua Sci. Technol.

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Motion tracking via Inertial Measurement Units (IMUs) on mobile and wearable devices has attracted significant interest in recent years. High-accuracy IMU-tracking can be applied in various applications, such as indoor navigation, gesture recognition, text input, etc. Many efforts have been devoted to improving IMU-based motion tracking in the last two decades, from early calibration techniques on ships or airplanes, to recent arm motion models used on wearable smart devices. In this paper, we present a comprehensive survey on IMU-tracking techniques on mobile and wearable devices. We also reveal the key challenges in IMU-based motion tracking on mobile and wearable devices and possible directions to address these challenges.

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Section: Related Workmentioning

confidence: 99%

Inertial motion tracking on mobile and wearable devices: Recent advancements and challenges

Song

Cao

et al. 2021

Tsinghua Sci. Technol.

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“…Few authors suggest methods where a 3-axis sensor can be calibrated using another sensor or orientation estimation obtained from a previously calibrated reference MIMU [29].For example [30]suggest a rate gyro calibration using magnetometer and [31] proposes accelerometer calibration using rate gyro. Similarly [32] performs a gyroscope aided magnetometer calibration.…”

Section: Calibration Using Reference Sensormentioning

confidence: 99%

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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“…If the effect from these types of errors are non-negligible, it is advised to perform a more sophisticated pre-calibration of the sensors to compensate for these errors. Methods for in-field pre-calibration of such errors exist; see, e.g., [30][31][32][33].…”

Section: Inertial Measurement Modelsmentioning

confidence: 99%

Robust Plug-and-Play Joint Axis Estimation Using Inertial Sensors

Olsson

Kok

Seel

et al. 2020

Sensors

Self Cite

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Inertial motion capture relies on accurate sensor-to-segment calibration. When two segments are connected by a hinge joint, for example in human knee or finger joints as well as in many robotic limbs, then the joint axis vector must be identified in the intrinsic sensor coordinate systems. Methods for estimating the joint axis using accelerations and angular rates of arbitrary motion have been proposed, but the user must perform sufficiently informative motion in a predefined initial time window to accomplish complete identifiability. Another drawback of state of the art methods is that the user has no way of knowing if the calibration was successful or not. To achieve plug-and-play calibration, it is therefore important that 1) sufficiently informative data can be extracted even if large portions of the data set consist of non-informative motions, and 2) the user knows when the calibration has reached a sufficient level of accuracy. In the current paper, we propose a novel method that achieves both of these goals. The method combines acceleration- and angular rate information and finds a globally optimal estimate of the joint axis. Methods for sample selection, that overcome the limitation of a dedicated initial calibration time window, are proposed. The sample selection allows estimation to be performed using only a small subset of samples from a larger data set as it deselects non-informative and redundant measurements. Finally, an uncertainty quantification method that assures validity of the estimated joint axis parameters, is proposed. Experimental validation of the method is provided using a mechanical joint performing a large range of motions. Angular errors in the order of 2 ∘ were achieved using 125–1000 selected samples. The proposed method is the first truly plug-and-play method that overcome the need for a specific calibration phase and, regardless of the user’s motions, it provides an accurate estimate of the joint axis as soon as possible.

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Accelerometer calibration using sensor fusion with a gyroscope

Cited by 20 publications

References 12 publications

Inertial motion tracking on mobile and wearable devices: Recent advancements and challenges

Inertial motion tracking on mobile and wearable devices: Recent advancements and challenges

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

Robust Plug-and-Play Joint Axis Estimation Using Inertial Sensors

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