Leveraging Semantic Maps for City-Scale Cross-View Localization

2026-07-27

AI Summary

This work addresses the challenge of cross-view localization for robots operating in unseen urban environments using publicly available semantic maps such as OpenStreetMap. The proposed method leverages a vision-language model (VLM) to extract semantic landmarks from panoramic images and employs a lightweight matcher—distilled from the VLM via knowledge distillation—to efficiently align these observations with large-scale semantic maps. Temporal pose estimates are refined through Bayesian filtering. Trained solely on daytime data from a single city, the framework demonstrates robust generalization across eleven diverse environmental conditions, including nighttime and blizzard scenarios, and scales to map areas spanning hundreds of square kilometers. The authors also release a semantic dataset and source code to support reproducibility and further research.

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 https://efahnestock.github.io/loci/.

Problem

• cross-view localization
• semantic maps
• city-scale
• robot localization
• OpenStreetMap

Innovation

• semantic map
• cross-view localization
• vision-language models
• knowledge distillation
• Bayesian filtering