Coarse-to-Fine Monocular Re-Localization in OpenStreetMap via Semantic Alignment

• URL: http://arxiv.org/abs/2603.01613v1
• Date: Mon, 02 Mar 2026 08:43:43 GMT
• Title: Coarse-to-Fine Monocular Re-Localization in OpenStreetMap via Semantic Alignment
• Abstract summary: We propose a hierarchical search framework with semantic alignment for localization in OpenStreetMap.<n>Our method significantly improves both localization accuracy and speed.<n>When trained on a single dataset, the 3 orientation recall of our method even outperforms the 5 recall of state-of-the-art methods.
• Score: 11.415441412121474
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Monocular re-localization plays a crucial role in enabling intelligent agents to achieve human-like perception. However, traditional methods rely on dense maps, which face scalability limitations and privacy risks. OpenStreetMap (OSM), as a lightweight map that protects privacy, offers semantic and geometric information with global scalability. Nonetheless, there are still challenges in using OSM for localization: the inherent cross-modal discrepancies between natural images and OSM, as well as the high computational cost of global map-based localization. In this paper, we propose a hierarchical search framework with semantic alignment for localization in OSM. First, the semantic awareness capability of DINO-ViT is utilised to deconstruct visual elements to establish semantic relationships with OSM. Second, a coarse-to-fine search paradigm is designed to replace global dense matching, enabling efficient progressive refinement. Extensive experiments demonstrate that our method significantly improves both localization accuracy and speed. When trained on a single dataset, the 3° orientation recall of our method even outperforms the 5° recall of state-of-the-art methods.

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