Evidential Semantic Mapping in Off-Road Environments with Uncertainty-Aware Bayesian Kernel Inference

Robotic mapping with Bayesian Kernel Infer- ence (BKI) has shown promise in creating semantic maps by effectively leveraging local spatial information. However, exist- ing semantic mapping methods face challenges in constructing reliable maps in unstructured outdoor scenarios due to unreli- able semantic predictions. To address this issue, we propose an evidential semantic mapping, which can enhance reliability in perceptually challenging off-road environments. We integrate Evidential Deep Learning into the semantic segmentation net- work to obtain the uncertainty estimate of semantic prediction. Subsequently, this semantic uncertainty is incorporated into an uncertainty-aware BKI, tailored to prioritize more confident semantic predictions when accumulating semantic information. By adaptively handling semantic uncertainties, the proposed framework constructs robust representations of the surround- ings even in previously unseen environments. Comprehensive experiments across various off-road datasets demonstrate that our framework enhances accuracy and robustness, consistently outperforming existing methods in scenes with high perceptual uncertainties.