Direct Method of IMU Calibration

Ilewicz, Witold; Nawrat, Aleksander

doi:10.1007/978-3-642-31665-4_13

Cited by 10 publications

(9 citation statements)

References 8 publications

(5 reference statements)

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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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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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“…The IMU modules comprise accelerometers and gyroscopes to record acceleration and give raw outputs from A/D converters. 20 IMU recordings are subject to drift of the sensitivity and offset, causing the need to be calibrated. 21 Calibration for inertial sensors generally compares the raw sensor output to a known quantity from physical phenomenon generated by calibration set ups and instruments.…”

Section: Validation Of Hardwarementioning

confidence: 99%

Development of a system for monitoring performance in the strength and conditioning domain

Gordon

Lopik

Hayward

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part P:

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The development hardware and software aspects of a novel system for monitoring performance in the strength and conditioning domain are summarized in this paper. A development framework is presented to enable the capture of development processes and support detailed comparison of performance monitoring systems. The framework couples the established Computer Integrated Manufacture Open Systems Architecture (CIMOSA) with Unified Modelling Language (UML) to document both the software development and implementation. Corrective algorithms are proposed that have increased the accuracy of the IMU outputs in monitoring the performance of a countermovement jump. Lastly, a wireless Inertial Measurement Unit (IMU) has been validated for use within the strength and conditioning domain by comparison with an optical motion capture reference measure.

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“…However, a standard manual calibration can also be applied before localization. In a manual calibration [14], [15], a 3D rotation of several 2D rotations are performed to collect magnetic filed data and use the least squares fitting method to recover the parameters of the ellipsoid and further correct the raw data.…”

Section: Potential Improvementmentioning

confidence: 99%

“…The angel error after calibration is reduce to 1.96°on average. Compared with the angle calibration with extensive user involvement, such as rotating the smartphone 360° [14], [15], the proposed geometric projection constrained calibration approach can achieve high accuracy in a rather lightweight way.…”

Section: Calibration Performancementioning

confidence: 99%

Visible Light Positioning Using A Single LED Luminaire

2019

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Visible light positioning (VLP) has developed rapidly with the ubiquitous lighting infrastructure in recent years. Based on classic trilateration or triangulation, VLP usually requires at least three light sources which makes it infeasible in some scenarios where sparse LED luminaries are mounted. In this paper, we focus on VLP in such scenarios and propose a VLP method based on a single LED luminaire as the light transmitter and commercial off-the-shelf smartphone as the receiver. A critical problem that needs to be addressed is to derive the accurate orientation of the smartphone. Although the inertial measurement unit of the smartphone can be used to obtain the orientation, the measurement error severely degrades the positioning accuracy. To address the measurement error, this paper innovates in exploiting projective geometry to calibrate the orientation measurements. We build an experimental platform to evaluate the proposed VLP method. The experimental results show that the proposed VLP method can achieve a submeter accuracy and the indoor positioning errors are within 0.16 m by 90%.

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Direct Method of IMU Calibration

Cited by 10 publications

References 8 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

Development of a system for monitoring performance in the strength and conditioning domain

Visible Light Positioning Using A Single LED Luminaire

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