利用地图先验与WiFi信号，实现大空间高精度室内定位
WiFi-based Global Localization in Large-Scale Environments Leveraging Structural Priors from osmAG
- 结合OSM地图几何拓扑先验与信号传播建模，优化AP位置
- 在线定位误差低至3.12米（指纹区）和3.83米（非指纹区）
- 无需指纹数据即可定位，适合无预先采集数据场景
全局定位对自主机器人至关重要，尤其在无GPS信号的室内环境。本文提出一种基于WiFi的定位框架，利用普遍存在的无线基础设施与开放街图区域图（osmAG），融合信号传播模型与osmAG的几何与拓扑先验。离线阶段通过迭代优化算法建模墙体衰减，实现接入点（AP）平均定位误差3.79米（比三边测量提升35.3%）。在线阶段使用增强的osmAG地图进行实时定位，在指纹区域平均误差为3.12米（比KNN指纹法提升8.77%），在非指纹区域误差为3.83米（提升81.05%）。相比传统指纹法，本方法更省空间且精度更高，尤其适用于无指纹数据的位置。在11,025平方米多楼层复杂环境中验证，该框架具备可扩展性、低成本优势，有效解决“被劫持机器人”问题。代码与数据集已开源。
原文摘要 · Abstract (English)
Global localization is essential for autonomous robotics, especially in indoor environments where the GPS signal is denied. We propose a novel WiFi-based localization framework that leverages ubiquitous wireless infrastructure and the OpenStreetMap Area Graph (osmAG) for large-scale indoor environments. Our approach integrates signal propagation modeling with osmAG's geometric and topological priors. In the offline phase, an iterative optimization algorithm localizes WiFi Access Points (APs) by modeling wall attenuation, achieving a mean localization error of 3.79 m (35.3\% improvement over trilateration). In the online phase, real-time robot localization uses the augmented osmAG map, yielding a mean error of 3.12 m in fingerprinted areas (8.77\% improvement over KNN fingerprinting) and 3.83 m in non-fingerprinted areas (81.05\% improvement). Comparison with a fingerprint-based method shows that our approach is much more space efficient and achieves superior localization accuracy, especially for positions where no fingerprint data are available. Validated across a complex 11,025 &m^2& multi-floor environment, this framework offers a scalable, cost-effective solution for indoor robotic localization, solving the kidnapped robot problem. The code and dataset are available at https://github.com/XuMa369/osmag-wifi-localization.
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