UFO: Uncertainty-aware LiDAR-image Fusion for Off-road Semantic Terrain Map Estimation
<|reference_start|>UFO: Uncertainty-aware LiDAR-image Fusion for Off-road Semantic Terrain Map Estimation: 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.<|reference_end|>
@article{kim2024ufo:,
    title={UFO: Uncertainty-aware LiDAR-image Fusion for Off-road Semantic Terrain
  Map Estimation},
    author={Ohn Kim, Junwon Seo, Seongyong Ahn, Chong Hui Kim},
    journal={arXiv preprint arXiv:2403.02642},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.02642},
    primaryClass={cs.RO cs.CV}
}