外观
论文arXiv 2604.016442 个月前
TOL：基于 OpenStreetMap 的文本定位
原文链接：TOL: Textual Localization With OpenStreetMap（arXiv 2604.01644 · 发布日期：2026-08-21）
TOL: Textual Localization With OpenStreetMap
提出T2O文本到OSM定位任务与TOL基准，并用粗到细TOLoc在多城市道路轨迹上验证定位性能。
核心要点
- 面向无几何观测或GNSS初始位置的城市2D定位，探索文本描述到OSM定位。
- 构建TOL大规模基准，含约121K文本查询、OSM瓦片和约316 km道路轨迹。
- TOLoc以方向感知特征检索候选位置，再融合文本与局部地图回归2-DoF位姿。
- 实验在5/10/25 m阈值分别较最佳现有方法提升6.53%、9.93%、8.32%。
验证范围与局限：验证范围为TOL基准中的Boston、Karlsruhe和Singapore城市道路轨迹；数据集、代码和模型将公开。
英文摘要 (Abstract)
Natural language provides an intuitive way to express spatial intent in geospatial applications. While existing localization methods often rely on dense point cloud maps or high-resolution imagery, OpenStreetMap (OSM) offers a compact and freely available map representation that encodes rich semantic and structural information, making it well-suited for large-scale localization. However, text-to-OSM (T2O) localization remains largely unexplored. In this paper, we formulate the T2O localization task, which aims to estimate accurate 2D positions in urban environments from textual scene descriptions without relying on geometric observations or GNSS-based initial location. To support the proposed task, we introduce TOL, a large-scale benchmark spanning multiple continents and diverse urban environments. TOL contains approximately 121K textual queries paired with OSM map tiles and covers about 316 km of road trajectories across Boston, Karlsruhe, and Singapore. We further propose TOLoc, a coarse-to-fine localization framework that explicitly models the semantics of surrounding objects and their directional information. In the coarse stage, direction-aware features are extracted from both textual descriptions and OSM tiles to construct global descriptors, which are used to retrieve candidate locations for the query. In the fine stage, the query text and top-1 retrieved tile are jointly processed, where a dedicated alignment module fuses the textual descriptor and local map features to regress the 2-DoF pose. Experimental results demonstrate that TOLoc achieves strong localization performance, outperforming the best existing method by 6.53%, 9.93%, and 8.32% at 5 m, 10 m, and 25 m thresholds, respectively, and shows strong generalization to unseen environments. Dataset, code and models will be publicly available at: https://github.com/WHU-USI3DV/TOL.
作者：Youqi Liao, Shuhao Kang, Jingyu Xu, Olaf Wysocki, Yan Xia, Jianping Li, Zhen Dong, Bisheng Yang, Xieyuanli Chen
来源
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