Vehicle Heading Enhancement Based on Adaptive Sliding Window Factor Graph Optimization for Gyroscope/Magnetometer

When employing gyroscope/magnetometer integration for vehicle heading estimation, the challenge of time-varying random magnetic interference in the environment frequently arises, resulting in reduced accuracy in heading estimation. To address this issue, this article presents an approach to enhance vehicle heading estimation based on adaptive sliding window factor graph optimization (ASWFGO) for gyroscope/magnetometer integration. The method calculates the magnetometer angular velocity by taking the differential of the magnetometer heading. It introduces the difference between the magnetometer and gyroscope angular velocities as a feature to detect random magnetic interference and applies variance threshold optimization to adaptively adjust the sliding window length. This ultimately achieves an optimal heading estimation within the sliding window. The experimental results demonstrate the effectiveness of the proposed algorithm in improving the accuracy of vehicle heading estimation in complex random magnetic interference environments.