用导航标志和公开地图实现机器人无需建图的精准定位
SignLoc: Robust Localization using Navigation Signs and Public Maps
- 通过提取地图中的导航图，匹配检测到的标志信息
- 仅需1-2个标志即可在大型环境中稳定定位
- 适合无先验地图的复杂室内场景应用
导航标志和地图（如楼层平面图和街景地图）广泛存在于人类活动环境，是辅助导航的重要资源，但极少被机器人系统利用。本文提出SignLoc，一种基于导航标志与公开地图（包括楼层平面图和OpenStreetMap图）进行全局定位的方法，无需预先依赖传感器构建地图。SignLoc首先从输入地图中提取导航图，再采用概率观测模型将检测到的标志方向与位置信息匹配到该图，在蒙特卡洛框架下实现鲁棒的拓扑语义定位。我们在大学校园、购物中心和医院等多个大规模环境中进行了评估，实验结果表明，机器人仅需观察1至2个导航标志即可可靠完成定位。
原文摘要 · Abstract (English)
Navigation signs and maps, such as floor plans and street maps, are widely available and serve as ubiquitous aids for way-finding in human environments. Yet, they are rarely used by robot systems. This paper presents SignLoc, a global localization method that leverages navigation signs to localize the robot on publicly available maps -- specifically floor plans and OpenStreetMap (OSM) graphs -- without prior sensor-based mapping. SignLoc first extracts a navigation graph from the input map. It then employs a probabilistic observation model to match directional and locational cues from the detected signs to the graph, enabling robust topo-semantic localization within a Monte Carlo framework. We evaluated SignLoc in diverse large-scale environments: part of a university campus, a shopping mall, and a hospital complex. Experimental results show that SignLoc reliably localizes the robot after observing only one to two signs.
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