Determination of time delays in measurement channels during SINS calibration in inertial mode

Klimkovich, B. V.; Tolochko, A. M.

doi:10.1134/s2075108716020048

Cited by 5 publications

(3 citation statements)

References 2 publications

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“…In order to solve the problem of LiDAR-IMU time delay calibration, we present a fusion method based on ICP and an iterated sigma point Kalman filter (ISPKF), which combines the advantages of ICP and ISPKF [21,22,23]. The total least squares cost function is used by the ICP algorithm for registration, which allows us to merge the IMU orientation measurement uncertainty in a principled way, and to reduce the longer time intervals due to the accumulated noise effects by integrated LiDAR orientation measurement.…”

Section: Introductionmentioning

confidence: 99%

LiDAR-IMU Time Delay Calibration Based on Iterative Closest Point and Iterated Sigma Point Kalman Filter

Liu

2017

Sensors

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The time delay calibration between Light Detection and Ranging (LiDAR) and Inertial Measurement Units (IMUs) is an essential prerequisite for its applications. However, the correspondences between LiDAR and IMU measurements are usually unknown, and thus cannot be computed directly for the time delay calibration. In order to solve the problem of LiDAR-IMU time delay calibration, this paper presents a fusion method based on iterative closest point (ICP) and iterated sigma point Kalman filter (ISPKF), which combines the advantages of ICP and ISPKF. The ICP algorithm can precisely determine the unknown transformation between LiDAR-IMU; and the ISPKF algorithm can optimally estimate the time delay calibration parameters. First of all, the coordinate transformation from the LiDAR frame to the IMU frame is realized. Second, the measurement model and time delay error model of LiDAR and IMU are established. Third, the methodology of the ICP and ISPKF procedure is presented for LiDAR-IMU time delay calibration. Experimental results are presented that validate the proposed method and demonstrate the time delay error can be accurately calibrated.

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Section: Introductionmentioning

confidence: 99%

LiDAR-IMU Time Delay Calibration Based on Iterative Closest Point and Iterated Sigma Point Kalman Filter

Liu

2017

Sensors

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“…The angular rates are also set as 10 • s −1 , 30 • s −1 , and 50 • s −1 , respectively, and then the final results are obtained by calculating the mean values. Besides, we also calculate the asynchronous parameters following the method of [17,18], which is a one-parameter indirect calibration model (1-PICM). The statistical results are shown in table 6.…”

Section: Compensation Of the Time Asynchronous Parametersmentioning

confidence: 99%

“…The final results show that the timeasynchrony errors between gyros and accelerometers are the primary sources of time-asynchrony errors, not the same type of sensors. A Kalman filter has been proposed for calibrating the time asynchronous parameters in [17,18]. The error parameters can be identified entirely, but this method is limited in simulation.…”

Section: Introductionmentioning

confidence: 99%

A time asynchronous parameters calibration method of high-precision FOG-IMU based on a single-axis continuous rotation scheme

Chang¹,

Shiwei²,

Zhang³

et al. 2023

Meas. Sci. Technol.

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There is a time asynchronous problem with the output signals between each inertial sensor in an actual high-precision fiber optic gyro inertial measurement unit (FOG-IMU), which reduces the velocity update accuracy of the strap-down inertial navigation system (SINS) in the actual applications. Firstly, the velocity error formulas are derived in the coordinate navigation system considering three time asynchronous errors between the three-axis accelerometers and gyros. Then an error-state Kalman filter is designed with respect to the time asynchronous parameters measurement model in angular dynamic environments. Afterward, a single-axis continuous rotation scheme is developed to calibrate the time asynchronous parameters. In particular, a simple observability analysis model is proposed to verify the rationality of the calibration scheme. Furthermore, the time asynchronous parameters are calculated effectively based on the proposed indirect calibration scheme. Finally, the proposed method is verified effectively through simulations and turntable experiments, which can significantly reduce the measurement errors of high-precision FOG-IMU. As a result, compared with the traditional direct 3-parameter calibration method, the eastward and northward velocity errors are reduced by 7.01% and 2.48%, respectively.

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Precise Calibration of Strapdown INS Using Low-Accuracy Turntables

Klimkovich

2022

Gyroscopy Navig.

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Determination of time delays in measurement channels during SINS calibration in inertial mode

Cited by 5 publications

References 2 publications

LiDAR-IMU Time Delay Calibration Based on Iterative Closest Point and Iterated Sigma Point Kalman Filter

LiDAR-IMU Time Delay Calibration Based on Iterative Closest Point and Iterated Sigma Point Kalman Filter

A time asynchronous parameters calibration method of high-precision FOG-IMU based on a single-axis continuous rotation scheme

Precise Calibration of Strapdown INS Using Low-Accuracy Turntables

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