Terrain Mapping

Terrain mapping focuses on creating accurate and reliable representations of ground surfaces, crucial for diverse applications like autonomous navigation and environmental monitoring. Current research emphasizes probabilistic methods, incorporating uncertainty quantification through techniques like Gaussian processes and Hidden Markov Models, alongside the use of deep learning architectures (e.g., neural radiance fields, UNet-based ensembles) for efficient and robust terrain reconstruction from various data sources (LiDAR, imagery). These advancements improve the accuracy and reliability of terrain models, enabling safer autonomous systems and more effective environmental analysis, particularly in challenging or dynamic environments.