Purely Inertial Navigation with a Low-Cost MEMS Sensor Array

Blocher, Lukas; Mayer, Wolfram; Arena, Marco; Radović, Dušan; Hiller, Tobias; Gerlach, Joachim; Bringmann, Oliver

doi:10.1109/inertial51137.2021.9430468

Cited by 30 publications

(4 citation statements)

References 7 publications

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“…Blocher et al conducted navigation experiments using an array of 14 IMUs to address this issue. They were able to improve navigation accuracy by calibrating the scale factor, offset, and cross-axis sensitivity of the IMU devices [22]. However, the improvement in dynamic performance was less than expected.…”

Section: Introductionmentioning

confidence: 99%

Error Compensation Method for Pedestrian Navigation System Based on Low-Cost Inertial Sensor Array

Cao,

Luo,

Liu

et al. 2024

Sensors

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In the pedestrian navigation system, researchers have reduced measurement errors and improved system navigation performance by fusing measurements from multiple low-cost inertial measurement unit (IMU) arrays. Unfortunately, the current data fusion methods for inertial sensor arrays ignore the system error compensation of individual IMUs and the correction of position information in the zero-velocity interval. Therefore, these methods cannot effectively reduce errors and improve accuracy. An error compensation method for pedestrian navigation systems based on a low-cost array of IMUs is proposed in this paper. The calibration method for multiple location-free IMUs is improved by using a sliding variance detector to segment the angular velocity magnitude into stationary and motion intervals, and each IMU is calibrated independently. Compensation is then applied to the velocity residuals in the zero-velocity interval after zero-velocity update (ZUPT). The experimental results show a significant improvement in the average noise performance of the calibrated IMU array, with a 3.01-fold increase in static noise performance. In the closed-loop walking experiment, the average horizontal position error of a single calibrated IMU is reduced by 27.52% compared to the uncalibrated IMU, while the calibrated IMU array shows a 2.98-fold reduction in average horizontal position error compared to a single calibrated IMU. After compensating for residual velocity, the average horizontal position error of a single IMU is reduced by 0.73 m, while that of the IMU array is reduced by 64.52%.

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Section: Introductionmentioning

confidence: 99%

Error Compensation Method for Pedestrian Navigation System Based on Low-Cost Inertial Sensor Array

Cao,

Luo,

Liu

et al. 2024

Sensors

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show abstract

“…After the random errors of this array of MEMS sensors are fused through recursive Kalman filtering, the average error of the gyroscope array's angular velocity decreased from 0.6279 • s −1 to 0.0461 • s −1 , and the variance error decreased from 0.0260 • s −1 to 0.0002 • s −1 [6]. L. Blocher used an array fusion of 14 low-cost BMI090L sixaxis MEMS-IMUs to reduce its noise coefficient by approximately √ 14 times [7]. Kamata H used an array of 32 consumergrade MEMS-IMUs and evaluated their performance.…”

Section: Introductionmentioning

confidence: 99%

Method for measuring non-stationary motion attitude based on MEMS-IMU array data fusion and adaptive filtering

Lan,

Wang,

Song

et al. 2024

Meas. Sci. Technol.

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The error characteristics of a typical commercial MEMS-IMU were analyzed. Using 15 ICM-42688 sensors, a 3*5 MEMS-IMU array was designed, and a weighted data fusion method based on Allan variance for the MEMS-IMU array was proposed. This effectively reduces the random measurement errors of the MEMS-IMU, providing a data foundation for the precise measurement of motion and attitude of robots, vehicles, aircraft, and other systems under low-cost conditions. The text describes a measurement method for non-stationary motion attitudes of sports vehicles based on adaptive Kalman-Mahony. Specifically, it first uses adaptive Kalman filtering on array sensor data to calculate the measurement noise in real-time and adaptively adjust the filtering gain. Then, it determines the compensation coefficient of the accelerometer to the gyroscope angular velocity based on the motion state of the vehicle, and solves the attitude through complementary filtering to obtain the motion attitude quaternion. Finally, it converts it into the roll angle, pitch angle, and yaw angle of the sports vehicle. Experimental results show that the proposed MEMS-IMU array weighted data fusion method based on Allan variance has significant advantages over traditional single MEMS-IUM methods and traditional average weighting methods in reducing sensor angle random walk and zero drift instability. The proposed adaptive Kalman-Mahony attitude measurement method also shows a significant improvement in the accuracy of non-stationary motion attitude measurement compared to traditional methods.

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“…INS (inertial navigation system) is a device that calculates navigation information such as position, velocity, and attitude using measurements of the IMU (inertial measurement unit) [ 1 , 2 , 3 , 4 ]. INS is widely used in various fields such as weapons systems, robots, and aerospace because it can calculate accurate navigation information without the help of external sensors.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Gyro Bias Depending on the Position of Inertial Measurement Unit in Rotational Inertial Navigation Systems

Seo

Ryu

et al. 2022

Sensors

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In this paper, a calibration method for gyro bias that changes depending on the position of the IMU (inertial measurement unit) is proposed to improve the navigation performance of RLG-based RINS (ring-laser-gyro-based rotational inertial navigation system). RINS is a navigation device that compensates for the inertial sensor errors by utilizing the rotation of the IMU. In previous studies, the rotation scheme of the IMU is designed assuming that inertial sensor errors are not affected by position of the IMU. However, changes in temperature distribution, direction of gravity, and dithering according to the rotation of the IMU affect the inertial sensor errors, such as gyro bias. These errors could degrade the long-term navigation performance of RLG-based RINS. To deal with this problem, this paper proposed a compensation method of the gyro bias that changes depending on the position of the IMU. First, RINS is reviewed using a dual-axis 16-position rotation scheme and RLG. Next, the attitude error of RLG-based RINS is derived utilizing navigation equations. The effect of the gyro bias change caused by the change in the IMU attitude for the navigation performance of RINS is analyzed based on navigation equations and simulations. Finally, system-level indirect calibrations for the Z–axis up position and Z–axis down position are performed to calculate the gyro bias change caused by the IMU attitude. The accuracy of the proposed calibration method is verified by long-term navigation test. The test results show that the proposed calibration method improves the navigation performance of RINS compared with the conventional calibration method.

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Purely Inertial Navigation with a Low-Cost MEMS Sensor Array

Cited by 30 publications

References 7 publications

Error Compensation Method for Pedestrian Navigation System Based on Low-Cost Inertial Sensor Array

Error Compensation Method for Pedestrian Navigation System Based on Low-Cost Inertial Sensor Array

Method for measuring non-stationary motion attitude based on MEMS-IMU array data fusion and adaptive filtering

Analysis of Gyro Bias Depending on the Position of Inertial Measurement Unit in Rotational Inertial Navigation Systems

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