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This paper introduces a hybrid Unscented Kalman Filter (UKF) for unmanned ground vehicle (UGV) navigation that uses a deep neural network to predict process and measurement noise covariance matrices. The network is trained using a sim2real approach, relying solely on simulated data to learn noise characteristics from raw inertial and GNSS measurements. Experiments across three diverse datasets demonstrate a 12.7% improvement in position accuracy compared to an adaptive model-based approach, highlighting the method's robustness and generalization ability.
A neural network can learn to predict the noise covariance matrices of an Unscented Kalman Filter, improving UGV navigation accuracy by 12.7% even when trained only on simulated data.
Modern autonomous navigation for unmanned ground vehicles relies on different estimators to fuse inertial sensors and GNSS measurements. However, the constant noise covariance matrices often struggle to account for dynamic real-world conditions. In this work we propose a hybrid estimation framework that bridges classical state estimation foundations with modern deep learning approaches. Instead of altering the fundamental unscented Kalman filter equations, a dedicated deep neural network is developed to predict the process and measurement noise uncertainty directly from raw inertial and GNSS measurements. We present a sim2real approach, with training performed only on simulative data. In this manner, we offer perfect ground truth data and relieves the burden of extensive data recordings. To evaluate our proposed approach and examine its generalization capabilities, we employed a 160-minutes test set from three datasets each with different types of vehicles (off-road vehicle, passenger car, and mobile robot), inertial sensors, road surface, and environmental conditions. We demonstrate across the three datasets a position improvement of $12.7\%$ compared to the adaptive model-based approach. Thus, offering a scalable and a more robust solution for unmanned ground vehicles navigation tasks.
