Evidential Semantic Mapping in Off-road Environments with
  Uncertainty-aware Bayesian Kernel Inference
Junyoung Kim Junwon Seo Jihong Min
Robotic mapping with Bayesian Kernel Inference (BKI) has shown promise in
creating semantic maps by effectively leveraging local spatial information.
However, existing semantic mapping methods face challenges in constructing
reliable maps in unstructured outdoor scenarios due to unreliable 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 network 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 surroundings 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.