Methods comparison for attitude determination of a lightweight buoy by raw data of IMU

Jouybari, Arash; Amiri, Hadi; Ardalan, Alireza A.; zahraee, N. Khodaverdi

doi:10.1016/j.measurement.2018.11.061

Cited by 20 publications

(11 citation statements)

References 14 publications

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“…The microelectromechanical systems-based (MEMS-based) relative localization problem is a recent topic, which has been widely investigated in many areas including robotics and control [1][2][3][4][5][6][7][8], healthcare and rehabilitation [9][10][11], consumer electronics mobile devices [12][13][14], and automated driving and navigation [15][16][17][18], both in industry and in scientific research. Independent from the application, accurate and robust attitude estimation is a crucial task to be solved, especially if the results are to be incorporated into unstable closed-loop systems, such as the control algorithms of mobile robots and unmanned aerial vehicles (UAVs) [1].…”

Section: Survey On Attitude Estimationmentioning

confidence: 99%

“…This implementation method does not include any explicit models of external disturbances. Instead, the effects of external disturbances are absorbed by v k in Equation (15); that is, the additive noise is characterized by a significantly larger noise variance in disturbed environments.…”

Section: Accelerometer and Magnetometer Modelsmentioning

confidence: 99%

“…The MARG sensor-based attitude realizations described by Equations (8) and (15) are utilized in a sensor fusion algorithm, which both synthesize the individual advantages and features of each sensor and provides attitude results with higher reliability and accuracy. First, this sensor fusion algorithm utilizes the gyroscope-based realization to propagate the attitude results, then these results are updated with the most recent quaternion realization derived from accelerometer and magnetometer readings.…”

Section: Attitude Estimation With Extended Kalman Filtermentioning

confidence: 99%

“…The filter has been improved recently in [7], employing the accelerometer and magnetometer measurements in a gradient descent algorithm to correct the quaternion obtained through the integration of rate measurements. Mahony and Madgwick filters are widely utilized algorithms and their performances have regularly been considered in comparative Sensors 2020, 20, 803 3 of 29 analyses [9,13,15,[31][32][33]. In [34], an adaptive-gain CF was proposed to provide good estimates, even in dynamic or high-frequency situations.…”

mentioning

confidence: 99%

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A Novel Fuzzy-Adaptive Extended Kalman Filter for Real-Time Attitude Estimation of Mobile Robots

Odry

Kecskés

Šarčević

et al. 2020

Sensors

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This paper proposes a novel fuzzy-adaptive extended Kalman filter (FAEKF) for the real-time attitude estimation of agile mobile platforms equipped with magnetic, angular rate, and gravity (MARG) sensor arrays. The filter structure employs both a quaternion-based EKF and an adaptive extension, in which novel measurement methods are used to calculate the magnitudes of system vibrations, external accelerations, and magnetic distortions. These magnitudes, as external disturbances, are incorporated into a sophisticated fuzzy inference machine, which executes fuzzy IF-THEN rules-based adaption laws to consistently modify the noise covariance matrices of the filter, thereby providing accurate and robust attitude results. A six-degrees of freedom (6 DOF) test bench is designed for filter performance evaluation, which executes various dynamic behaviors and enables measurement of the true attitude angles (ground truth) along with the raw MARG sensor data. The tuning of filter parameters is performed with numerical optimization based on the collected measurements from the test environment. A comprehensive analysis highlights that the proposed adaptive strategy significantly improves the attitude estimation quality. Moreover, the filter structure successfully rejects the effects of both slow and fast external perturbations. The FAEKF can be applied to any mobile system in which attitude estimation is necessary for localization and external disturbances greatly influence the filter accuracy.The MEMS inertial measurement unit (IMU), composed of tri-axis MEMS accelerometer, gyroscope, and magnetometer sensors, also known as the measurement system of magnetic, angular rate, and gravity (MARG) sensor arrays, is the most commonly utilized device to track the real-time orientation of mobile platforms at present. The low-cost, low power consumption, and small size characteristics meet technological requirements, and therefore these devices have been widely utilized in embedded systems, where the filtering algorithm is executed by a microprocessor. As a result, an attitude and heading reference system (AHRS) has been formed, which provides the complete orientation measurement relative to the Earth's gravitational and magnetic fields (global reference system), where the attitude denotes the roll and pitch angles, whereas heading refers to the yaw Euler angle [19]. The role of the aforementioned filtering algorithm is to combine the individual features of each sensor and provide both properly smoothed and robust attitude results with regard to the global reference system, in either Euler angles or quaternions. The most common method applied in sensor fusion techniques synthesizes the short-term accuracy of gyroscope-based attitude realizations and the accelerometer and magnetometer provide rough, low-frequency attitude corrections. This technique cancels the accumulated error (drift), smooths the signals, and produces long-term stable outputs if the IMU is in stationary states. Significant decrease in estimation performance arises when...

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Section: Survey On Attitude Estimationmentioning

confidence: 99%

Section: Accelerometer and Magnetometer Modelsmentioning

confidence: 99%

Section: Attitude Estimation With Extended Kalman Filtermentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

A Novel Fuzzy-Adaptive Extended Kalman Filter for Real-Time Attitude Estimation of Mobile Robots

Odry

Kecskés

Šarčević

et al. 2020

Sensors

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show abstract

“…However, the gyroscope calculates the orientation angles in triad orthogonal directions by integrating the angular rate in unit time. Accordingly, integrating in unit time causes cumulative errors; thus, the gyroscope's observables are only reliable in the short term (Jouybari et al 2019). The roll, pitch and yaw angles are used for transforming a vector from the body coordinate into an image coordinate system and vice versa.…”

Section: Introductionmentioning

confidence: 99%

Comparison of the strip- and block-wise aerial triangulation using different exterior orientation parameters weights

Jouybari

Bagherbandi

Nilfouroushan

2021

Journal of Spatial Science

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In this study, three different procedures: checkpoint RMS of residuals, statistical evaluation of AT results using t-test, and comparison of a photogrammetric digital surface model (DSM) and LiDAR data are used to analyse the effect of IMU and GNSS uncertainties on the final adjusted results. The outcome suggests that the method of block-wise GNSS shift correction is the better method for aerial triangulation and one should use appropriate observable weights in AT. The comparison of checkpoint RMS residuals between the two methods shows that the block-wise solution is on average 6cm more accurate than the strip-wise solution.

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Evaluation of Biomechanical Conditions in Infants from 20 to 48 Months of Age in Gait

Armas

Cabrera

López

2023

Lecture Notes in Networks and Systems

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Methods comparison for attitude determination of a lightweight buoy by raw data of IMU

Cited by 20 publications

References 14 publications

A Novel Fuzzy-Adaptive Extended Kalman Filter for Real-Time Attitude Estimation of Mobile Robots

A Novel Fuzzy-Adaptive Extended Kalman Filter for Real-Time Attitude Estimation of Mobile Robots

Comparison of the strip- and block-wise aerial triangulation using different exterior orientation parameters weights

Evaluation of Biomechanical Conditions in Infants from 20 to 48 Months of Age in Gait

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