# OpenStreetMap-based LiDAR Global Localization in Urban Environment without a Prior LiDAR Map

arXiv: https://arxiv.org/abs/2202.07516 · HTML: https://mlsanity.com/paper/2202.07516/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Feb 2022.

## Built on by (most cited first, 11 of 11)

- [General Place Recognition Survey: Towards Real-World Autonomy](https://mlsanity.com/paper/2405.04812/) (2024)
- [SemVecNet: Generalizable Vector Map Generation for Arbitrary Sensor Configurations](https://mlsanity.com/paper/2405.00250/) (2024)
- [SegLocNet: Multimodal Localization Network for Autonomous Driving via Bird's-Eye-View Segmentation](https://mlsanity.com/paper/2502.20077/) (2025)
- [OSM vs HD Maps: Map Representations for Trajectory Prediction](https://mlsanity.com/paper/2311.02305/) (2023)
- [Robust Self-Tuning Data Association for Geo-Referencing Using Lane Markings](https://mlsanity.com/paper/2207.14042/) (2022)
- [L2RSI: Cross-view LiDAR-based Place Recognition for Large-scale Urban Scenes via Remote Sensing Imagery](https://mlsanity.com/paper/2503.11245/) (2025)
- [A Survey on Visual Map Localization Using LiDARs and Cameras](https://mlsanity.com/paper/2208.03376/) (2022)
- [Geo-Localization Based on Dynamically Weighted Factor-Graph](https://mlsanity.com/paper/2311.07301/) (2023)
- [InterKey: Cross-modal Intersection Keypoints for Global Localization on OpenStreetMap](https://mlsanity.com/paper/2509.13857/) (2025)
- [TOL: Textual Localization with OpenStreetMap](https://mlsanity.com/paper/2604.01644/) (2026)
- [Aerial-ground LiDAR place recognition with patch-level self-supervised learning and expanded reciprocal re-ranking](https://mlsanity.com/paper/2606.18583/) (2026)

## Abstract (arXiv)

Using publicly accessible maps, we propose a novel vehicle localization method that can be applied without using prior light detection and ranging (LiDAR) maps. Our method generates OSM descriptors by calculating the distances to buildings from a location in OpenStreetMap at a regular angle, and LiDAR descriptors by calculating the shortest distances to building points from the current location at a regular angle. Comparing the OSM descriptors and LiDAR descriptors yields a highly accurate vehicle localization result. Compared to methods that use prior LiDAR maps, our method presents two main advantages: (1) vehicle localization is not limited to only places with previously acquired LiDAR maps, and (2) our method is comparable to LiDAR map-based methods, and especially outperforms the other methods with respect to the top one candidate at KITTI dataset sequence 00.