Evidential Semantic Mapping in Off-road Environments with Uncertainty-aware Bayesian Kernel Inference

arXiv:2403.14138v1 Announce Type: cross
Abstract: 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 …

abstract arxiv bayesian challenges cs.cv cs.ro environments face however inference information issue kernel mapping maps predictions robotic semantic spatial type uncertainty unstructured