An Optimization-Based Initial Alignment and Calibration Algorithm of Land-Vehicle SINS In-Motion.

Area of Science:

• Robotics and Autonomous Systems
• Navigation and Control Systems
• Sensor Fusion

Background:

• Strapdown Inertial Navigation Systems (SINS) for land vehicles require simultaneous self-calibration and attitude alignment.
• Existing methods often need prior information about attitude or sensor noise, limiting their applicability.

Purpose of the Study:

• To develop a complete alignment algorithm for land vehicle navigation using Inertial Measurement Units (IMUs) and an odometer.
• To enable simultaneous self-calibration of the odometer scale factor and IMU misalignment angle.
• To achieve self-initialization and calibration without prior attitude or sensor noise data.

Main Methods:

• A novel self-calibration algorithm based on global observability analysis.
• An optimal algorithm for initial alignment and calibration to estimate attitude and system parameters.
• Integration of IMUs and odometer data for enhanced navigation.

Main Results:

• The proposed algorithm demonstrates superior performance compared to the extended Kalman filter (EKF) during oscillating attitude motions.
• Computer simulations validate the effectiveness of the self-initialization and calibration capabilities.
• Vehicle tests confirm the practical advantages and robustness of the new algorithm.

Conclusions:

• The developed algorithm provides a robust solution for simultaneous attitude alignment and self-calibration in land vehicle SINS.
• It eliminates the need for prior sensor information, making it highly practical for real-world applications.
• The method offers significant performance improvements over traditional approaches, especially in dynamic scenarios.