  Autonomous off-road navigation requires an accurate semantic understanding of
the environment, often converted into a bird's-eye view (BEV) representation
for various downstream tasks. While learning-based methods have shown success
in generating local semantic terrain maps directly from sensor data, their
efficacy in off-road environments is hindered by challenges in accurately
representing uncertain terrain features. This paper presents a learning-based
fusion method for generating dense terrain classification maps in BEV. By
performing LiDAR-image fusion at multiple scales, our approach enhances the
accuracy of semantic maps generated from an RGB image and a single-sweep LiDAR
scan. Utilizing uncertainty-aware pseudo-labels further enhances the network's
ability to learn reliably in off-road environments without requiring precise 3D
annotations. By conducting thorough experiments using off-road driving
datasets, we demonstrate that our method can improve accuracy in off-road
terrains, validating its efficacy in facilitating reliable and safe autonomous
navigation in challenging off-road settings.
