Comparison of Bingham Filter and Extended Kalman Filter in IMU Attitude Estimation

Wang, Weixin; Adamczyk, Peter G.

doi:10.1109/jsen.2019.2922321

Cited by 14 publications

(4 citation statements)

References 21 publications

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“…To further improve the accuracy of pose estimation, studies focusing on enhancing classical pose estimation algorithms and considering sensor characteristics [22][23][24][25] have yielded promising results. In addition, introducing new sensors for specific application scenarios can improve estimation accuracy.…”

Section: Introductionmentioning

confidence: 99%

An improved attitude estimation algorithm for suppressing magnetic vector disturbance based on extended Kalman filter

Zong,

Su,

Gao

et al. 2024

Meas. Sci. Technol.

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This paper proposes an improved attitude estimation algorithm based on the extended Kalman filter (EKF), and it is applied to suppress the accuracy reduction in attitude estimation caused by fusing magnetometer data under large angular motion. In the proposed attitude estimation structure, the approximate variance of the estimated horizontal northbound magnetic vector is used to dynamically adjust the participation of magnetometer data in attitude estimation, as the approximate variance increases significantly under large angular motion and fusing magnetometer data will reduce estimation accuracy. A three-axis position-velocity controlled turntable is used to conduct rocking experiments for validating the proposed attitude estimation algorithm. The results show a significant improvement in yaw angle estimation accuracy with the proposed attitude estimation algorithm and correspondingly enhance the distribution of pitch and roll angle errors.

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Section: Introductionmentioning

confidence: 99%

An improved attitude estimation algorithm for suppressing magnetic vector disturbance based on extended Kalman filter

Zong,

Su,

Gao

et al. 2024

Meas. Sci. Technol.

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“…In AHRS, the reference vectors, namely Earth's gravity, and the Earth's magnetic field are derived from accelerometer and magnetometer measurements. The vectors are then fused to correct the attitude errors accumulated from integration of noisy gyroscope measurements, where sensor fusion methods such as complementary filters (Mahony et al, 2008, Fourati et al, 2010, Wu et al, 2016, Kalman filters (Marins et al, 2001, Crassidis et al, 2007, Del Rosario et al, 2018 or recent Bingham filter (Gilitschenski et al, 2015, Wang andAdamczyk, 2019) are usually employed. Most works assume that the body's acceleration is negligible and there is little electromagnetic interference that perturbs the measurable geomagnetic field (Del Rosario et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Indoor Attitude Estimation Using Equipped Gyroscopes and Depth Sensors

Shi

Song

Xiao³

et al. 2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Attitude estimation is central to a wide range of applications such as robotics, virtual reality and mobile smart devices. With the development of sensor technologies, these application devices are often equipped with gyroscopes and depth sensors. In this paper, we propose a novel method to fuse gyroscope and depth information for drift-free and robust attitude estimation in structured indoor applications. Our method relies on the depth information and the Manhattan world assumption to estimate the absolute orientation, which is then fused to correct the accumulated error of the gyroscope-determined attitude. We first utilize the mean shift algorithm on the unit sphere to align the surface normals from the depth measurements with the orthogonal planar structures of the Manhattan world. Therefore, the orientation estimates are drift-free and absolute with respect to the Manhattan world. We then fuse the orientation estimates with the gyroscope measurements in an error-state Kalman filter manner to further improve the attitude estimation accuracy and robustness. We validate the performance of our method on public datasets, demonstrating the robustness and accuracy of the method for attitude estimation.

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“…The magnetometer is usually not required for the inclinometer because the magnetic data is used for heading estimation. Meanwhile, acceleration and angular rates are necessary for the tilt calculation [26]. Practically, many inclinometer companies have been demanding an effective filter for only accelerometer without the support from other sensors because of economic concern when numerous inclinometers are in production.…”

Section: Introductionmentioning

confidence: 99%

A New Technique on Vibration Optimization of Industrial Inclinometer for MEMS Accelerometer Without Sensor Fusion

Hoang

Pietrosanto

2021

IEEE Access

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Accelerometer of the Microelectromechanical systems (MEMS) based inertial measurement units (IMUs) is key to inclination measurement in the industry 4.0. However, external vibration negatively impacts the precision of orientation angles during operation. Many inclinometer companies have demanded to develop a solution for vibration impact on accelerometer without other sensors' support because of economic problems. This paper presents a new algorithm Orientation Axes Crossover Processing (OACP) on vibration optimization for MEMS accelerometer without sensor fusion. The proposed filter works on a principle based on the characteristics of vibration impact on whether the X-axis or Y-axis to optimally minimize the noise. A high accurate setup is built-up based on the Pan-Tilt Unit and a TUMAC vibrator for the verification of new filters, implemented into LSM9DS1 (3D accelerometer, 3D gyroscope). The new filter is able to work independently, and also fuse with the Low-pass filter or Kalman filter to enhance the dynamic response, only 0.163 seconds as maximum delay during vibration. The experimental results show that the proposed algorithm always accomplishes smaller variations than Low-pass filter, about 0.2 degrees in standard deviation. The compromise between vibration immunity and dynamic response is analyzed in detail to demonstrate the optimal performances of the concerned filters. The project was carried out at the 'Sensor System' in Italy which is an industrial company in the inclinometer field.

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Comparison of Bingham Filter and Extended Kalman Filter in IMU Attitude Estimation

Cited by 14 publications

References 21 publications

An improved attitude estimation algorithm for suppressing magnetic vector disturbance based on extended Kalman filter

An improved attitude estimation algorithm for suppressing magnetic vector disturbance based on extended Kalman filter

Indoor Attitude Estimation Using Equipped Gyroscopes and Depth Sensors

A New Technique on Vibration Optimization of Industrial Inclinometer for MEMS Accelerometer Without Sensor Fusion

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