SignLoc: Robust Localization using Navigation Signs and Public Maps
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914012768239616 |
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| author | Zimmerman, Nicky Loo, Joel Agrawal, Ayush Hsu, David |
| author_facet | Zimmerman, Nicky Loo, Joel Agrawal, Ayush Hsu, David |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_18606 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SignLoc: Robust Localization using Navigation Signs and Public Maps Zimmerman, Nicky Loo, Joel Agrawal, Ayush Hsu, David Robotics 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. |
| title | SignLoc: Robust Localization using Navigation Signs and Public Maps |
| topic | Robotics |
| url | https://arxiv.org/abs/2508.18606 |