A Study on Graph Optimization Method for GNSS/IMU Integrated Navigation System Based on Virtual Constraints.

Area of Science:

• Navigation Systems Engineering
• Robotics and Autonomous Systems
• Geomatics Engineering

Background:

• Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) integration is crucial for accurate navigation.
• Satellite occlusion and non-line-of-sight (NLOS) conditions degrade GNSS positioning accuracy, impacting integrated system performance.
• Existing methods struggle to effectively utilize historical pseudorange data during GNSS signal anomalies.

Purpose of the Study:

• To propose a novel graph optimization-based GNSS/IMU model incorporating virtual constraints.
• To enhance the integrity and continuity of navigation systems when facing GNSS signal degradation.
• To evaluate the performance of the proposed model against traditional methods using real-world data.

Main Methods:

• Development of a graph optimization model for GNSS/IMU integration.
• Introduction of virtual constraints derived from satellite ephemeris, previous positions, and pseudorange rates.
• Analysis of graph model marginalization and comparison with traditional GNSS/IMU and SLAM graph models.

Main Results:

• The proposed method achieved an RMSE error within 5% of real pseudorange measurements in instantaneous testing.
• Demonstrated a 30% improvement in horizontal RMSE accuracy over traditional graph optimization during a 10-second GNSS outage.
• Maintained system integrity and continuity despite simulated satellite signal anomalies.

Conclusions:

• The virtual constraint approach effectively addresses GNSS signal anomalies in integrated navigation systems.
• The proposed graph optimization model offers a robust and accurate alternative to conventional methods.
• The method shows significant potential for practical applications requiring reliable navigation under challenging conditions.