[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-84319-en":3,"doc-seo-84319-105":28,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":13,"seo_description":14,"update_tm":26,"read_time":27},84319,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","RadLoc Radar-based 3-DoF Global Localization via Fast Robust and Lightweight Spatial Descriptor","RadLoc presents a holistic radar sensor-based global localization pipeline that connects place recognition to 3-DoF pose estimation in a fast, robust, and lightweight end-to-end framework. The method accelerates pre-processing using 1D CA-CFAR filtering and exploits near-range dominance in spinning radar images to build a compact, range-aware descriptor. An efficient hierarchical coarse-to-fine retrieval strategy and phase correlation-based pose estimation enable use in SLAM and multi-session SLAM. Experiments on 15 sequences across 5 datasets confirm robust performance with the smallest descriptor size and fastest retrieval time.","RadLoc: Radar-based 3-DoF Global Localization via Fast, Robust, and Lightweight Spatial Descriptor Across Diverse Environmental Scenarios  \nHogyun Kim 1 , Jiwon Choi 1 , Jungwoo Lee 1 , and Younggun Cho 1†  \narXiv :2607 .08 1 15v 1 [ cs .RO] 9 Jul 2026  \nAbstract—While global localization using spinning radar has gained attention for its robustness to adverse weather and challenging environments, many studies have focused on individual components such as place recognition or pose estimation. In this paper, we take a holistic view of radar sensor-based global localization and present RadLoc, a fast, robust, and lightweight end-to-end pipeline from place recognition to 3-DoF pose estimation. RadLoc accelerates pre-processing using 1D CA-CFAR filtering and leverages the near-range dominance in spinning radar images to design a compact descriptor and an efficient hierarchical coarse-to-fine retrieval strategy. Moreover, coupled with phase correlation-based 3-DoF pose estimation, it forms a versatile global localization module applicable to SLAM and multi-session SLAM systems. Extensive experiments on 15 sequences across 5 datasets demonstrate that RadLoc achieves robust performance while maintaining the smallest descriptor size and fastest retrieval time among state-of-theart approaches. The supplementary materials are available at [https://sparolab.github.io/research/radloc/](https://sparolab.github.io/research/radloc/).  \nI. INTRODUCTION Global localization, which recognizes revisited places (i.e., place recognition) and estimates the relative pose between them, is a fundamental capability for autonomous mobile robots, underpinning applications such as simultaneous localization and mapping (SLAM) [1] and multi-session/multirobot mapping [2, 3] . Unlike vision and light detection and ranging (LiDAR) sensor-based approaches that suffer from short-wavelength attenuation under adverse weather, radio detection and ranging (radar) sensors penetrate rain, fog, snow, and dust due to their longer wavelength. For  \nthis reason, radar sensor-based global localization has been actively studied and has demonstrated its effectiveness across diverse environments and weather conditions [4] .  \nDespite remarkable progress [5–9] over the past decade, prior works have often focused on enhancing the performance of individual components (e.g., place recognition or pose estimation), disregarding computational efficiency and the integration into an overall global localization framework. In particular, while recent learning-based approaches [10, 11] have shown strong recognition performance, they often require extensive training data and substantial computational resources, limiting their deployability in new environments. While prior works primarily improve individual components, we instead adopt a holistic perspective on radar sensor-  \n1Hogyun Kim, 1Jiwon Choi, 1Jungwoo Lee, and 1†Younggun Cho are with the Electrical Engineering, Inha University, Incheon, South Korea [[hg.kim](hg.kim) , jiwon2, pihsdneirf]@ [inha.edu](inha.edu) , [yg.cho@inha.ac.kr](yg.cho@inha.ac.kr)  \nThis work was supported by National Research Foundation of Korea (NRF) grant (No. RS-2026-2555148 and RS-2025-02217000) and Institute of Information & communications Technology Planning & Evaluation (IITP) grant (RS-2022-II220448) funded by the Korea government (MSIT) .  \nOxford Bellmouth  \nHydro  \nMaree  \nBridge  \nMountain  \nSejong  \nKAIST  \nDCC  \nSnow  \nRain  \nFig. 1: Comparison of Recall@1 (top), descriptor size (bottomleft), and retrieval runtime (bottom-right) with state-of-the-art approaches [5–8, 10, 11] on 11 sequences across 5 datasets [5, 12– 15] . Despite variations in environments, weather conditions, and radar types, our RadLoc consistently demonstrates robust recall performance while achieving the smallest descriptor size and the fastest place recognition time (i.e., description generation time and retrieval time) among all compared methods.  \nbased global localization ","cbCaibjNOjSiKXtC","https://ap.wps.com/l/cbCaibjNOjSiKXtC","pdf",7684699,1,"English","en",105,"# Introduction\n# Related Works","[{\"question\":\"What is RadLoc and what problem does it address?\",\"answer\":\"RadLoc is an end-to-end radar-based global localization pipeline that performs place recognition and 3-DoF pose estimation. 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This combination enables an end-to-end pipeline for global localization tasks.\"}]",1784194792,20,{"code":4,"msg":29,"data":30},"ok",{"site_id":23,"language":22,"slug":31,"title":13,"keywords":32,"description":14,"schema_data":33,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":26},"radloc-radar-based-3-dof-global-localization-via-fast-robust-and-lightweight-spatial-descriptor","",{"@graph":34,"@context":84},[35,52,67],{"@type":36,"itemListElement":37},"BreadcrumbList",[38,42,46,49],{"item":39,"name":40,"@type":41,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":43,"name":44,"@type":41,"position":45},"https://docshare.wps.com/document/","Document",2,{"item":47,"name":12,"@type":41,"position":48},"https://docshare.wps.com/document/research-report/",3,{"item":50,"name":13,"@type":41,"position":51},"https://docshare.wps.com/document/radloc-radar-based-3-dof-global-localization-via-fast-robust-and-lightweight-spatial-descriptor/84319/",4,{"url":50,"name":13,"@type":53,"author":54,"headline":13,"publisher":56,"fileFormat":59,"inLanguage":22,"description":14,"dateModified":60,"datePublished":61,"encodingFormat":59,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":55},"Person",{"url":39,"name":57,"@type":58},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",true,{"@type":64,"interactionType":65,"userInteractionCount":20},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What is RadLoc and what problem does it address?","Question",{"text":74,"@type":75},"RadLoc is an end-to-end radar-based global localization pipeline that performs place recognition and 3-DoF pose estimation. It addresses limitations of prior work that focused on isolated components without integrating computation efficiency and pose estimation into a unified framework.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does RadLoc improve speed during pre-processing and retrieval?",{"text":79,"@type":75},"RadLoc replaces costly feature extraction with 1D CA-CFAR filtering along the range direction. It also uses a compact descriptor with a hierarchical coarse-to-fine retrieval strategy to speed up place recognition.",{"name":81,"@type":72,"acceptedAnswer":82},"How is 3-DoF pose estimation performed in RadLoc?",{"text":83,"@type":75},"RadLoc couples the global localization module with phase correlation-based 3-DoF pose estimation. 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