SignLoc: Robust Localization using Navigation Signs and Public Maps

To localize, it matches these cues to a large-scale, indoor-outdoor navigation graph, constructed from publicly available maps. Abstract -- 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. Y et, 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. 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. Localizing and navigating in the open world remains a challenge for robots due to the diversity and complexity of human environments.