融合深度与语义的概率平面图定位
Fusion of Depth and Semantics for Probabilistic Floorplan Localization
将深度与语义以概率分布形式统一融合进射线匹配框架，并用 Jensen-Shannon 散度保留语义歧义而非硬标签化，这一思路可迁移到其他需要融合多模态不确定性的位姿估计与匹配任务。
原文依据 we propose a floorplan-aware probabilistic fusion framework that models both depth and semantic information within a unified architecture
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论平面图定位旨在估计查询图像相对于二维平面图的相机位姿，为室内机器人与增强现实提供了一种轻量且长期稳定的替代方案，可取代基于三维地图或大型图像数据库的定位方法。近期方法将该问题建模为基于射线的匹配，即把图像表示为一组带有深度或语义标签标注的射线，并将其与平面图对齐。然而，这些方法在应对室内环境的复杂性时仍面临挑战，这种复杂性可分解为环境歧义、几何歧义与语义歧义。为应对这些歧义，我们提出了一种平面图感知的概率融合框架，在统一架构中同时建模深度与语义信息。该框架还将基于分布的射线置信度估计器与基于 Jensen-Shannon 散度（JSD）的概率语义匹配方案相结合：前者降低不确定几何假设的权重，后者保留并利用有信息量的语义歧义，而非将其压缩为硬标签。在具有挑战性的基准上的实验表明，我们的方法在鲁棒性与精度上均显著优于先前方法。
英文原文摘要 1218 字符
Floorplan localization aims to estimate the camera pose of a query image with respect to a 2D floorplan, providing a lightweight and long-term stable alternative to localization based on 3D maps or large image databases for indoor robotics and AR. Recent methods frame the problem as ray-based matching, representing the image as a set of rays annotated with depth or semantic labels and aligning them with the floorplan. However, they still face challenges in addressing the complexity of indoor environments, which can be decomposed into environmental, geometric, and semantic ambiguities.To address these ambiguities, we propose a floorplan-aware probabilistic fusion framework that models both depth and semantic information within a unified architecture. Our framework also combines a distribution-based ray confidence estimator, which down-weights uncertain geometric hypotheses, with a probabilistic semantic matching scheme based on Jensen-Shannon divergence (JSD), which preserves and leverages informative semantic ambiguity instead of collapsing it into hard labels. Experiments on challenging benchmarks demonstrate that our approach significantly outperforms prior methods in both robustness and accuracy.