Leveraging AI and Hybrid Intelligence for Robust Geospatial Data Fusion in Autonomous Terrestrial Navigation

Rapid advancement of artificial intelligence (AI) and geospatial data fusion has enabled the development of highly autonomous terrestrial navigation systems with improved accuracy, adaptability, and robustness. This paper proposes a novel framework integrating multi-source geospatial data fusion with deep learning-based decision-making for autonomous terrestrial navigation. Unlike conventional approaches that rely solely on Global Navigation Satellite Systems (GNSS) or inertial sensors, our system leverages a hybrid fusion model combining GNSS, LiDAR, camera vision, and high-resolution geospatial databases. A deep reinforcement learning (DRL) paradigm is introduced to enhance the system’s adaptability in dynamic environments, optimizing route planning and obstacle avoidance in real-time. Additionally, a hybrid AI model incorporating Graph Neural Networks (GNN) and Transformer-based architectures processes spatial and temporal dependencies in navigation data, improving localization precision and resilience against sensor failures. The proposed system is evaluated through extensive simulations and real-world tests, demonstrating superior performance in complex urban and off-road scenarios compared to traditional Kalman filter-based methods. Our findings highlight the potential of AI-driven geospatial data fusion in redefining autonomous navigation, paving the way for next-generation intelligent mobility solutions.