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

TL;DR

This paper introduces an evidential semantic mapping framework that integrates uncertainty estimation into Bayesian Kernel Inference to improve the robustness and accuracy of semantic maps in challenging off-road environments.

Contribution

It presents a novel integration of Evidential Deep Learning with Bayesian Kernel Inference for uncertainty-aware semantic mapping in unstructured outdoor scenarios.

Findings

Enhanced map accuracy in high-uncertainty environments

Robustness to unseen off-road scenes

Outperforms existing semantic mapping methods

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 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.