Large-Scale Radar Localization using Online Public Maps

Ziyang Hong, Yvan Petillot, Kaicheng Zhang, Shida Xu, Sen Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we propose using online public maps, e.g., OpenStreetMap (OSM), for large-scale radar-based localization without needing a prior sensing map. This can potentially extend the localization system to anywhere worldwide without building, saving, or maintaining a sensing map, as long as an online public map covers the operating area. Existing methods using OSM only use route network or semantics information. These two sources of information are not combined in the previous works, while our proposed system fuses them to improve localization accuracy. Our experiments, on three open datasets collected from three different continents, show that the proposed system outperforms the state-of-the-art localization methods, reducing up to 50% of position errors. We release an open-source implementation for the community.

Original languageEnglish
Title of host publication2023 IEEE International Conference on Robotics and Automation (ICRA)
PublisherIEEE
Pages3990-3996
Number of pages7
ISBN (Electronic)9798350323658
DOIs
Publication statusPublished - 4 Jul 2023
Event2023 IEEE International Conference on Robotics and Automation - London, United Kingdom
Duration: 29 May 20232 Jun 2023

Conference

Conference2023 IEEE International Conference on Robotics and Automation
Abbreviated titleICRA 2023
Country/TerritoryUnited Kingdom
CityLondon
Period29/05/232/06/23

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