A Hybrid Neural-Assisted Unscented Kalman Filter for Unmanned Ground Vehicle Navigation

2026-03-12

AI Summary

This work addresses the limited accuracy of traditional unscented Kalman filters (UKF) in localizing unmanned ground vehicles within dynamic environments, a shortcoming primarily attributed to their reliance on fixed process and measurement noise covariances. To overcome this limitation, the authors propose a novel framework that integrates deep learning with UKF without altering its core equations. Specifically, a dedicated neural network is introduced to estimate the process and observation noise covariances in real time directly from raw inertial and GNSS measurements. Trained exclusively on simulated data, the model demonstrates effective sim-to-real transferability. Evaluated across 160 minutes of diverse test scenarios involving three distinct vehicle platforms and environmental conditions, the proposed method achieves a 12.7% improvement in average localization accuracy over existing adaptive approaches, substantially enhancing robustness, generalization, and practical applicability.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Local Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

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.

Problem

• Unmanned Ground Vehicle
• Navigation
• Noise Covariance
• State Estimation
• GNSS

Innovation

• Neural-Assisted Kalman Filter
• Noise Covariance Estimation
• Sim2Real Learning
• Unmanned Ground Vehicle Navigation
• Deep Learning for Sensor Fusion