用证据深度学习提升语义地图的不确定性感知能力
E2-BKI: Evidential Ellipsoidal Bayesian Kernel Inference for Uncertainty-aware Gaussian Semantic Mapping
- 融合证据深度学习与贝叶斯核推断，量化语义预测不确定性
- 在多种复杂户外场景中实现更精准、鲁棒的实时语义地图构建
- 适合需要高可靠性感知的机器人导航与自动驾驶研究者
语义映射旨在构建环境的三维语义表示，为复杂室外场景中的机器人运行提供关键知识。尽管贝叶斯核推断（BKI）能缓解稀疏传感器数据下的映射不连续问题，现有方法在挑战性室外环境中仍受多重不确定性影响。为此，我们提出一种不确定性感知的语义映射框架，可有效处理多种不确定性源，显著降低其对映射性能的负面影响。本方法利用证据深度学习估计语义预测的不确定性，并将其融入BKI以实现稳健的语义推断。进一步地，通过将噪声观测聚合为一致的高斯表示，减轻不可靠点的影响；同时采用几何对齐的自适应核函数，适配复杂场景结构。这些高斯原语有效融合局部几何与语义信息，实现在复杂室外场景下的鲁棒、不确定性感知映射。在多样化的非道路及城市室外环境中进行全面评估，结果表明该方法在映射质量、不确定性校准、表征灵活性和鲁棒性方面均有持续提升，且保持实时效率。
原文摘要 · Abstract (English)
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
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