Robust Vehicle Pose Estimation Through Multi-Sensor Fusion of Camera, IMU, and GPS Using LSTM and Kalman Filter

Accurate vehicle localization remains a critical challenge due to the frequent loss or degradation of sensor data, such as from visual, inertial, and GPS sources. In this study, we present a novel localization algorithm that dynamically fuses data from heterogeneous sensors to achieve stable and precise positioning. The proposed algorithm integrates a deep learning-based visual-inertial odometry (VIO) module with a Kalman filter for global data fusion. A key innovation of the method is its adaptive fusion strategy, which adjusts feature weights based on sensor reliability, thereby ensuring optimal data utilization. Extensive experiments across varied scenarios demonstrate the algorithm’s superior performance, consistently achieving lower RMSE values and reducing position errors by 79–91% compared to four state-of-the-art baselines—even under adverse conditions such as sensor failures or missing data. This work lays the foundation for deploying robust localization systems in real-world applications, including autonomous vehicles, robotics, and navigation technologies.