# Uncertainty-Aware Hierarchical Re-Localization in OpenStreetMap via Semantic Alignment

arXiv: https://arxiv.org/abs/2603.01613 · HTML: https://mlsanity.com/paper/2603.01613/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 39 references that are in the map.
- First posted on arXiv 2 Mar 2026.

## Builds on (most cited first, 27 of 27)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [Vision Transformers Need Registers](https://mlsanity.com/paper/2309.16588/) (2023)
- [From Coarse to Fine: Robust Hierarchical Localization at Large Scale](https://mlsanity.com/paper/1812.03506/) (2018)
- [Deep ViT Features as Dense Visual Descriptors](https://mlsanity.com/paper/2112.05814/) (2021)
- [Back to the Feature: Learning Robust Camera Localization from Pixels to Pose](https://mlsanity.com/paper/2103.09213/) (2021)
- [VIGOR: Cross-View Image Geo-localization beyond One-to-one Retrieval](https://mlsanity.com/paper/2011.12172/) (2020)
- [AnyLoc: Towards Universal Visual Place Recognition](https://mlsanity.com/paper/2308.00688/) (2023)
- [Where am I looking at? Joint Location and Orientation Estimation by Cross-View Matching](https://mlsanity.com/paper/2005.03860/) (2020)
- [CLIP-DINOiser: Teaching CLIP a few DINO tricks for open-vocabulary semantic segmentation](https://mlsanity.com/paper/2312.12359/) (2023)
- [Beyond Cross-view Image Retrieval: Highly Accurate Vehicle Localization Using Satellite Image](https://mlsanity.com/paper/2204.04752/) (2022)
- [OrienterNet: Visual Localization in 2D Public Maps with Neural Matching](https://mlsanity.com/paper/2304.02009/) (2023)
- [Where is your place, Visual Place Recognition?](https://mlsanity.com/paper/2103.06443/) (2021)
- [Loc-NeRF: Monte Carlo Localization using Neural Radiance Fields](https://mlsanity.com/paper/2209.09050/) (2022)
- [Visual Cross-View Metric Localization with Dense Uncertainty Estimates](https://mlsanity.com/paper/2208.08519/) (2022)
- [Text2Loc: 3D Point Cloud Localization from Natural Language](https://mlsanity.com/paper/2311.15977/) (2023)
- [OpenStreetMap: Challenges and Opportunities in Machine Learning and Remote Sensing](https://mlsanity.com/paper/2007.06277/) (2020)
- [Leveraging Deep Visual Descriptors for Hierarchical Efficient Localization](https://mlsanity.com/paper/1809.01019/) (2018)
- [The Revisiting Problem in Simultaneous Localization and Mapping: A Survey on Visual Loop Closure Detection](https://mlsanity.com/paper/2204.12831/) (2022)
- [You Are Here: Geolocation by Embedding Maps and Images](https://mlsanity.com/paper/1911.08797/) (2019)
- [Revisit Anything: Visual Place Recognition via Image Segment Retrieval](https://mlsanity.com/paper/2409.18049/) (2024)
- [MapLocNet: Coarse-to-Fine Feature Registration for Visual Re-Localization in Navigation Maps](https://mlsanity.com/paper/2407.08561/) (2024)
- [U-BEV: Height-aware Bird's-Eye-View Segmentation and Neural Map-based Relocalization](https://mlsanity.com/paper/2310.13766/) (2023)
- [LoD-Loc: Aerial Visual Localization using LoD 3D Map with Neural Wireframe Alignment](https://mlsanity.com/paper/2410.12269/) (2024)
- [A Survey on Monocular Re-Localization: From the Perspective of Scene Map Representation](https://mlsanity.com/paper/2311.15643/) (2023)
- [Map It Anywhere (MIA): Empowering Bird's Eye View Mapping using Large-scale Public Data](https://mlsanity.com/paper/2407.08726/) (2024)

## Abstract (arXiv)

Monocular re-localization enables robots to estimate camera poses from visual observations. However, many existing methods rely on dense maps or large reference image databases, which face scalability limitations and privacy risks. OpenStreetMap (OSM), as a lightweight privacy-preserving map, offers semantic and geometric information with global scalability. Nonetheless, OSM localization remains challenging due to cross-modal discrepancies between natural images and OSM, as well as the high cost of global map-based localization. In this paper, we propose an uncertainty-aware hierarchical search framework with semantic alignment for localization in OSM. First, object-centric DINO-ViT tokens are exploited to reduce the semantic gap between ground-view observations and OSM vectors. Second, global dense matching is decomposed into coarse FFT correlation and uncertainty-controlled local refinement. Extensive experiments demonstrate that our method significantly improves localization accuracy and speed. When trained on a single dataset, the 3^circ orientation recall of our method even outperforms the 5^circ recall of state-of-the-art methods.