On-the-fly geometric calibration of inertial sensor arrays

Carlsson, Håkan; Skog, Isaac; Jaldén, Joakim

doi:10.1109/ipin.2017.8115879

Cited by 11 publications

(2 citation statements)

References 15 publications

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“…The time samples were selected when there was a nonzero rotation of the array, since a non-rotating motion would not provide any information of the accelerometer positions. Guidelines of how to select informative samples can be found in [25], [26]. Fig.…”

Section: Methodsmentioning

confidence: 99%

Quantifying the Uncertainty of the Relative Geometry in Inertial Sensors Arrays

et al. 2021

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We present an algorithm to estimate and quantify the uncertainty of the accelerometers' relative geometry in an inertial sensor array. We formulate the calibration problem as a Bayesian estimation problem and propose an algorithm that samples the accelerometer positions' posterior distribution using Markov chain Monte Carlo. By identifying linear substructures of the measurement model, the unknown linear motion parameters are analytically marginalized, and the remaining non-linear motion parameters are numerically marginalized. The numerical marginalization occurs in a low dimensional space where the gyroscopes give information about the motion. This combination of information from gyroscopes and analytical marginalization allows the user to make no assumptions of the motion before the calibration. It thus enables the user to estimate the accelerometer positions' relative geometry by simply exposing the array to arbitrary twisting motion. We show that the calibration algorithm gives good results on both simulated and experimental data, despite sampling a high dimensional space.

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Section: Methodsmentioning

confidence: 99%

Quantifying the Uncertainty of the Relative Geometry in Inertial Sensors Arrays

et al. 2021

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“…2, which is an inertial sensor array with 32 IMUs attached to a Printed Circuit Board (PCB). The accelerometers and gyroscopes are assumed to be perfectly calibrated in terms of scale factors, misalignment, cross-coupling, and accelerometer positions [39], [47], [48]. Hence, the sensor measurements are assumed only to be corrupted by a constant bias and white noise.…”

Section: A Simulationsmentioning

confidence: 99%

Inertial Navigation Using an Inertial Sensor Array

Carlsson¹,

Skog²,

Hendeby³

et al. 2022

Preprint

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We present a comprehensive framework for fusing measurements from multiple and generally placed accelerometers and gyroscopes to perform inertial navigation. Using the angular acceleration provided by the accelerometer array, we show that the numerical integration of the orientation can be done with second-order accuracy, which is more accurate compared to the traditional first-order accuracy that can be achieved when only using the gyroscopes. Since orientation errors are the most significant error source in inertial navigation, improving the orientation estimation reduces the overall navigation error. The practical performance benefit depends on prior knowledge of the inertial sensor array, and therefore we present four different state-space models using different underlying assumptions regarding the orientation modeling. The models are evaluated using a Lie Group Extended Kalman filter through simulations and realworld experiments. We also show how individual accelerometer biases are unobservable and can be replaced by a six-dimensional bias term whose dimension is fixed and independent of the number of accelerometers.

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Analysis of influencing factors on fusion accuracy of virtual gyroscope technology and new data fusion method

Song

Shi

et al. 2020

ISA Transactions

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On-the-fly geometric calibration of inertial sensor arrays

Cited by 11 publications

References 15 publications

Quantifying the Uncertainty of the Relative Geometry in Inertial Sensors Arrays

Quantifying the Uncertainty of the Relative Geometry in Inertial Sensors Arrays

Inertial Navigation Using an Inertial Sensor Array

Analysis of influencing factors on fusion accuracy of virtual gyroscope technology and new data fusion method

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