  Semantic mapping with Bayesian Kernel Inference (BKI) has shown promise in
providing a richer understanding of environments by effectively leveraging
local spatial information. However, existing methods face challenges in
constructing accurate semantic maps or reliable uncertainty maps in
perceptually challenging environments due to unreliable semantic predictions.
To address this issue, we propose an evidential semantic mapping framework,
which integrates the evidential reasoning of Dempster-Shafer Theory of Evidence
(DST) into the entire mapping pipeline by adopting Evidential Deep Learning
(EDL) and Dempster's rule of combination. Additionally, the extended belief is
devised to incorporate local spatial information based on their uncertainty
during the mapping process. Comprehensive experiments across various off-road
datasets demonstrate that our framework enhances the reliability of uncertainty
maps, consistently outperforming existing methods in scenes with high
perceptual uncertainties while showing semantic accuracy comparable to the
best-performing semantic mapping techniques.
