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Robust Attitude Estimation for Low-Dynamic Vehicles Based on MEMS-IMU and External Acceleration Compensation.

Jiaxuan Chen1,2, Bingbo Cui1,2, Xinhua Wei1,2

  • 1Key Laboratory of Modern Agricultural Equipment and Technology, Jiangsu University, Ministry of Education, Zhenjiang 212013, China.

Sensors (Basel, Switzerland)
|July 27, 2024
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Summary
This summary is machine-generated.

This study introduces a robust Kalman filter (RKF) to enhance attitude estimation accuracy for vehicles using micro-electro-mechanical system inertial measurement units (MEMS-IMUs). The RKF adaptively compensates for external accelerations, significantly improving performance in challenging conditions.

Keywords:
dynamic attitude estimationexternal acceleration compensationinertial measurement unitrobust Kalman filter

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Area of Science:

  • Robotics and Control Systems
  • Sensor Fusion and Navigation
  • Mechanical Engineering

Background:

  • Micro-electro-mechanical system inertial measurement units (MEMS-IMUs) are crucial for attitude determination.
  • Non-gravitational accelerations significantly impact MEMS-IMU attitude estimation accuracy.
  • Existing methods struggle with low-dynamic vehicles under vibrations and uneven terrain.

Purpose of the Study:

  • To develop a robust Kalman filter (RKF) for improved MEMS-IMU attitude estimation.
  • To enhance the accuracy and reliability of attitude determination for low-dynamic vehicles.
  • To compensate for external acceleration disturbances in real-world conditions.

Main Methods:

  • Constructed a state model for MEMS-IMU attitude estimation using a simplified direction cosine matrix.
  • Developed an RKF that adaptively adjusts noise covariance to compensate for external acceleration.
  • Estimated unmodeled external acceleration variance online using filtering innovations.
  • Validated the RKF using experiments on a three-axis turntable, an automatic vehicle, and a tractor tillage test.

Main Results:

  • RKF achieved a root mean square error (RMSE) of 0.051° on a turntable, outperforming conventional Kalman Filter (KF) and MTi-300.
  • Dynamic attitude estimation for an automatic vehicle showed smoother pitch angles with RKF compared to KF, reducing RMSE from 0.875° to 0.460°.
  • Tractor tillage tests demonstrated a 47.5% improvement in plough pitch RMSE with RKF (0.259°) compared to KF (0.493°).

Conclusions:

  • The developed RKF effectively suppresses external acceleration disturbances in MEMS-IMU-based attitude estimation.
  • RKF offers superior performance and accuracy for attitude determination in challenging vehicle dynamics and environments.
  • The adaptive noise covariance compensation is key to the RKF's enhanced robustness and reliability.