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

TL;DR

This paper introduces a hierarchical, semantic-aware re-localization method in OpenStreetMap that improves accuracy and efficiency for monocular localization by leveraging a coarse-to-fine search and semantic alignment.

Contribution

It presents a novel coarse-to-fine hierarchical search framework with semantic alignment using DINO-ViT for improved monocular re-localization in OSM.

Findings

Significantly improves localization accuracy and speed.

Outperforms state-of-the-art methods in orientation recall.

Efficiently handles cross-modal discrepancies between images and OSM.

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{\deg} orientation recall of our method even outperforms the 5{\deg} recall of state-of-the-art methods.