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Attitude Estimation Algorithm of Portable Mobile Robot Based on Complementary Filter.

Mei Liu¹, Yuanli Cai¹, Lihao Zhang¹

¹School of Automation Science and Engineering, Xi'an Jiaotong University, Xi'an 710049, China.

|November 27, 2021

Summary

This summary is machine-generated.

This study compares complementary filtering algorithms for robot inertial navigation. The Mahony filter offers similar accuracy to the Extended Kalman Filter but with lower computational cost, making it ideal for embedded systems.

Keywords:

attitude estimation complementary filter extended Kalman filtering portable mobile robot quaternion implementation

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Area of Science:

Robotics
Navigation Systems
Sensor Fusion

Background:

Inertial navigation systems (INS) for robots suffer from gyroscope and accelerometer drift and noise.
Traditional filters like Extended Kalman Filter (EKF) and Particle Filter (PF) have high computational costs.
Accurate attitude estimation is crucial for robot navigation and control.

Purpose of the Study:

To evaluate complementary filtering algorithms for improving attitude estimation in robot INS.
To compare the accuracy and computational efficiency of different filtering techniques.
To identify a suitable algorithm for low-cost embedded robotic systems.

Main Methods:

Utilized quaternion representation for attitude motion.
Developed sensor and system models for inertial measurement units (IMUs).
Implemented and compared Quaternion Extended Kalman Filter (QEKF), Linear Complementary Filter (LCF), and Mahony Complementary Filter (MCF).

Main Results:

Mahony Complementary Filter (MCF) demonstrated significantly lower computational cost compared to Extended Kalman Filter (EKF).
Attitude estimation accuracy of MCF was comparable to that of QEKF.
Sensor fusion of IMU data effectively corrected attitude data for high-precision angle estimation.

Conclusions:

Mahony Complementary Filter is a computationally efficient and accurate solution for attitude estimation in robot INS.
MCF is well-suited for resource-constrained, low-cost embedded systems.
Complementary filtering effectively addresses drift and noise issues in IMU data for enhanced navigation.