Attitude Estimation Algorithm of Portable Mobile Robot Based on Complementary Filter

Liu, Mei; Cai, Yuanli; Zhang, Li-Hao; Wang, Yudong

doi:10.3390/mi12111373

Cited by 12 publications

(8 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the rise of frontier technologies such as navigation and positioning, autonomous driving, and personal wearable devices [13][14][15][16][17], the demand for inertial sensor accuracy is gradually increasing. In contrast, high-precision inertial sensors for high-end manufacturing (e.g., defense industry) are still difficult and costly to manufacture.…”

Section: Introductionmentioning

confidence: 99%

MEMS Inertial Sensor Calibration Technology: Current Status and Future Trends

Shang

et al. 2022

Micromachines

View full text Add to dashboard Cite

A review of various calibration techniques of MEMS inertial sensors is presented in this paper. MEMS inertial sensors are subject to various sources of error, so it is essential to correct these errors through calibration techniques to improve the accuracy and reliability of these sensors. In this paper, we first briefly describe the main characteristics of MEMS inertial sensors and then discuss some common error sources and the establishment of error models. A systematic review of calibration methods for inertial sensors, including gyroscopes and accelerometers, is conducted. We summarize the calibration schemes into two general categories: autonomous and nonautonomous calibration. A comprehensive overview of the latest progress made in MEMS inertial sensor calibration technology is presented, and the current state of the art and development prospects of MEMS inertial sensor calibration are analyzed with the aim of providing a reference for the future development of calibration technology.

show abstract

Section: Introductionmentioning

confidence: 99%

MEMS Inertial Sensor Calibration Technology: Current Status and Future Trends

Shang

et al. 2022

Micromachines

View full text Add to dashboard Cite

show abstract

“…This approach contributes to enhancing the precision of the entire navigation system. When passing through a tunnel, although the

signal is blocked and the

information is inaccurate, the attitude information in the TMR digital compass is accurate and can continue to provide attitude correction for the

[ 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 ].…”

Section: Design Of High-precision Portable Digital Compass Systemmentioning

confidence: 99%

Development and Application of a High-Precision Portable Digital Compass System for Improving Combined Navigation Performance

Zhang,

Cui,

Zhang

2024

Sensors

View full text Add to dashboard Cite

There are not many high-precision, portable digital compass solutions available right now that can enhance combined navigation systems’ overall functionality. Additionally, there is a dearth of writing about these products. This is why a tunnel magnetoresistance (TMR) sensor-based high-precision portable digital compass system is designed. First, the least-squares method is used to compensate for compass inaccuracy once the ellipsoid fitting method has corrected manufacturing and installation errors in the digital compass system. Second, the digital compass’s direction angle data is utilized to offset the combined navigation system’s mistake. The final objective is to create a high-performing portable TMR digital compass system that will enhance the accuracy and stability of the combined navigation system (abbreviated as CNS). According to the experimental results, the digital compass’s azimuth accuracy was 4.1824° before error compensation and 0.4580° after it was applied. The combined navigation system’s path is now more accurate overall and is closer to the reference route than it was before the digital compass was added. Furthermore, compared to the combined navigation route without the digital compass, the combined navigation route with the digital compass included is more stable while traveling through the tunnel. It is evident that the digital compass system’s design can raise the integrated navigation system’s accuracy and stability. The integrated navigation system’s overall performance may be somewhat enhanced by this approach.

show abstract

“…However, due to the existence of noise, zero bias and integration error, the angle value obtained by gyroscope integration [30] has a large error when the system is moving. Complementary filtering [31,32] is a method that uses the output of the accelerometer in a specified time to correct the cumulative drift error of the gyroscope, so as to obtain the attitude of the system with high confidence.…”

Section: Tight Fusion Based On Complementary Filteringmentioning

confidence: 99%

A tightly-coupled method of lidar-inertial based on complementary filtering

Liu

Zheng

Jia

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

In the application of small field angle Lidar for robot SLAM (Simultaneous Localization and Mapping), livox mapping can provide accurate odometer information and point cloud information of the environment with good precision for the robot in a short time. However, over long periods of motion, the laser odometer calculated by livox mapping will produce a large offset, which will reduce the localization accuracy and mapping accuracy of the robot. To overcome above problem, a lidar-inertial navigation odometer compact fusion method based on the idea of complementary filtering is proposed in this paper. By taking advantage of the good static performance of the accelerometer for a long time, the angle value obtained by the gyroscope integration is corrected. In the back-end optimization, the jacobian matrix obtained by the residual calculation of the acceleration in the navigation coordinate system obtained by IMU and the gravitational acceleration is tightly coupled with the jacobian matrix of the lidar residual. Different weights are given to the residual of each part, and the odometer is solved iteratively to further improve the pose accuracy of the whole SLAM system. In this paper, the method is applied to Livox-Mid40. The experimental results show that it can reduce the drift of long time and long distance and improve the accuracy of the system localization and mapping.

show abstract

Attitude Estimation Algorithm of Portable Mobile Robot Based on Complementary Filter

Cited by 12 publications

References 7 publications

MEMS Inertial Sensor Calibration Technology: Current Status and Future Trends

MEMS Inertial Sensor Calibration Technology: Current Status and Future Trends

Development and Application of a High-Precision Portable Digital Compass System for Improving Combined Navigation Performance

A tightly-coupled method of lidar-inertial based on complementary filtering

Contact Info

Product

Resources

About