Kalman-filter-based orientation determination using inertial/magnetic sensors: observability analysis and performance...

Area of Science:

• Robotics
• Sensor Fusion
• Navigation Systems

Background:

• Accurate 3D orientation estimation is crucial for robotics and navigation.
• Inertial Measurement Units (IMUs) combined with magnetic sensors offer a cost-effective solution.
• Challenges include magnetic disturbances and sensor biases.

Purpose of the Study:

• To develop a quaternion-based Extended Kalman Filter (EKF) for robust 3D orientation estimation.
• To model and compensate for magnetic disturbances and gyro bias errors.
• To analyze the observability conditions for reliable motion tracking.

Main Methods:

• Utilized a quaternion representation for orientation.
• Integrated measurements from an Inertial Measurement Unit (IMU) and a tri-axial magnetic sensor.
• Employed the observability rank criterion based on Lie derivatives to determine system observability.
• Modeled magnetic disturbances and gyro bias within the filter state.

Main Results:

• The proposed EKF effectively estimates 3D orientation by compensating for sensor errors.
• Observability conditions require non-collinear gravity and perturbed magnetic vectors, along with IMU angular motion.
• Simulations and experiments validate the algorithm's performance, even under critical observability conditions.

Conclusions:

• The quaternion-based EKF provides a robust solution for 3D orientation estimation using IMUs.
• Understanding and meeting observability conditions are essential for accurate and bounded estimation errors.
• The method demonstrates effectiveness in real-world scenarios with magnetic disturbances.