Evidential Semantic Mapping in Off-road Environments with Uncertainty-aware Bayesian Kernel Inference
This work addresses the challenge of constructing reliable semantic maps for robotics in perceptually challenging off-road environments, representing an incremental improvement over existing methods.
The paper tackled the problem of unreliable semantic predictions in unstructured outdoor environments by integrating Evidential Deep Learning with Bayesian Kernel Inference to prioritize confident predictions, resulting in enhanced accuracy and robustness across various off-road datasets.
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.