Leveraging Semantic Maps for City-Scale Cross-View Localization
This paper proposes a robot localization method that leverages rich semantic data from OpenStreetMap using VLMs to extract landmarks from panoramas and match them with prior maps, distilling a lightweight matcher for efficiency, and demonstrating generalization across diverse environments including snowstorms.
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[Submitted on 28 Jul 2026]
Title:Leveraging Semantic Maps for City-Scale Cross-View Localization
View a PDF of the paper titled Leveraging Semantic Maps for City-Scale Cross-View Localization, by Ethan Fahnestock and Erick Fuentes and Philip R Osteen and Nicholas Roy
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Abstract:We want robots to localize in previously untraversed environments against commonly available prior data. Rich semantic data available from OpenStreetMap can be useful in this task. However, existing methods either ignore this semantic information, directly matching panoramas and overhead imagery, or dramatically compress the semantic information, working with a small set of fixed classes. To leverage this rich semantic information, two challenges need to be overcome. First, useful semantic information needs to be extracted from the robot's egocentric observations. Second, the observed information must be quickly associated with the large prior semantic map (e.g., up to 628 km^2). We show that VLMs are effective at both extracting relevant landmarks from panoramas, and identifying feasible correspondences between these landmarks and prior overhead landmarks. However, using VLMs to propose all correspondences scales poorly as the number of mapped landmarks increases. Instead, we propose distilling a lightweight matcher from a VLM which computes correspondences for all entities in a map. We use this output to form an observation likelihood which is fused over time with a Bayes filter to create a time series of pose estimates. To support further investigation into generalizable cross-view methods that leverage semantic information, we release a dataset of extracted semantics and evaluation trajectories spanning eleven environments, including panoramas we collected in a snowstorm and at night in Boston. We demonstrate our method, trained on a single city's fair-weather data, generalizes across location, lighting, weather, and other challenges. Code and datasets are available at this https URL.
Comments: Equal contribution by Ethan Fahnestock and Erick Fuentes. 13 pages, 7 figures, and 5 tables
Subjects:
Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2607.25215 [cs.RO]
(or arXiv:2607.25215v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.25215
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Ethan Fahnestock [view email] [v1] Tue, 28 Jul 2026 02:44:56 UTC (7,771 KB)
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