{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/loosely-coupled-odometry-uwb-ranging-and","title":"Fusing Odometry, UWB Ranging, and Spatial Detections for Relative Multi-Robot Localization","arxiv_id":"2304.06264","date":"2023-04-13","proceeding":null,"authors":["Xianjia Yu","Iacopo Catalano","Paola Torrico Morón","Sahar Salimpour","Tomi Westerlund","Jorge Peña Queralta"],"abstract":"This letter presents a cooperative relative multi-robot localization design and experimental study. We propose a flexible Monte Carlo approach leveraging a particle filter to estimate relative states. The estimation can be based on inter-robot Ultra-Wideband (UWB) ranging and onboard odometry alone or dynamically integrated with cooperative spatial object detections from stereo cameras mounted on each robot. The main contributions of this work are as follows. First, we show that a single UWB range is enough to estimate the accurate relative states of two robots when fusing odometry measurements. Second, our experiments also demonstrate that our approach surpasses traditional methods, namely, multilateration, in terms of accuracy. Third, to further increase accuracy, we allow for the integration of cooperative spatial detections. Finally, we show how ROS 2 and Zenoh can be integrated to build a scalable wireless communication solution for multi-robot systems. The experimental validation includes real-time deployment and autonomous navigation based on the relative positioning method. It is worth mentioning that we also address the challenges for UWB-ranging error mitigation for mobile transceivers. The code is available at https://github.com/TIERS/uwb-cooperative-mrs-localization.","url_abs":"https://arxiv.org/abs/2304.06264v2","url_pdf":"https://arxiv.org/pdf/2304.06264v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"loosely-coupled-odometry-uwb-ranging-and","repo_url":"https://github.com/tiers/uwb-cooperative-mrs-localization","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"loosely-coupled-odometry-uwb-ranging-and","repo_url":"https://github.com/tiers/uwb-relative-localization-dataset","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}