Semantic mapping aims to construct a 3D semantic representation of the environment, providing essential knowledge for robots operating in complex outdoor settings. While Bayesian Kernel Inference (BKI) addresses discontinuities of map inference from sparse sensor data, existing semantic mapping methods suffer from various sources of uncertainties in challenging outdoor environments. To address these issues, we propose an uncertainty-aware semantic mapping framework that handles multiple sources of uncertainties, which significantly degrade mapping performance. Our method estimates uncertainties in semantic predictions using Evidential Deep Learning and incorporates them into BKI for robust semantic inference. It further aggregates noisy observations into coherent Gaussian representations to mitigate the impact of unreliable points, while employing geometry-aligned kernels that adapt to complex scene structures. These Gaussian primitives effectively fuse local geometric and semantic information, enabling robust, uncertainty-aware mapping in complex outdoor scenarios. Comprehensive evaluation across diverse off-road and urban outdoor environments demonstrates consistent improvements in mapping quality, uncertainty calibration, representational flexibility, and robustness, while maintaining real-time efficiency. Our project website: https://e2-bki.github.io

翻译：语义建图旨在构建环境的三维语义表示，为机器人在复杂户外场景中的运行提供关键知识。尽管贝叶斯核推理（BKI）能够处理稀疏传感器数据导致的建图推断不连续问题，但现有语义建图方法在具有挑战性的户外环境中仍面临多种不确定性来源的困扰。为解决这些问题，我们提出了一种不确定性感知语义建图框架，该框架能够处理显著降低建图性能的多种不确定性来源。我们的方法通过证据深度学习估计语义预测中的不确定性，并将其整合至BKI中以实现鲁棒的语义推断。该方法进一步将噪声观测聚合为连贯的高斯表示以减轻不可靠点的影响，同时采用适应复杂场景结构的几何对齐核函数。这些高斯基元能够有效融合局部几何与语义信息，从而在复杂户外场景中实现鲁棒且不确定性感知的建图。通过对多样化越野与城市户外环境的综合评估，本方法在建图质量、不确定性校准、表示灵活性和鲁棒性方面均展现出持续改进，同时保持实时运行效率。项目网站：https://e2-bki.github.io