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However, there is a lack of public datasets to study and benchmark UWB TDOA positioning technology in cluttered indoor environments. We fill in this gap by presenting a comprehensive dataset using Decawave\u2019s DWM1000 UWB modules. To characterize the UWB TDOA measurement performance under various line-of-sight (LOS) and non-line-of-sight (NLOS) conditions, we collected signal-to-noise ratio (SNR), power difference values, and raw UWB TDOA measurements during the identification experiments. We also conducted a cumulative total of around 150\u00a0min of real-world flight experiments on a customized quadrotor platform to benchmark the UWB TDOA localization performance for mobile robots. The quadrotor was commanded to fly with an average speed of 0.45\u00a0m\/s in both obstacle-free and cluttered environments using four different UWB anchor constellations. Raw sensor data including UWB TDOA, inertial measurement unit (IMU), optical flow, time-of-flight (ToF) laser altitude, and millimeter-accurate ground truth robot poses were collected during the flights. The dataset and development kit are available at https:\/\/utiasdsl.github.io\/util-uwb-dataset\/ . <\/jats:p>","DOI":"10.1177\/02783649241230640","type":"journal-article","created":{"date-parts":[[2024,2,5]],"date-time":"2024-02-05T20:42:40Z","timestamp":1707165760000},"page":"1443-1456","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":33,"title":["UTIL: An ultra-wideband time-difference-of-arrival indoor localization dataset"],"prefix":"10.1177","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4288-0582","authenticated-orcid":false,"given":"Wenda","family":"Zhao","sequence":"first","affiliation":[{"name":"The University of Toronto Robotics Institute, Vector Institute for Artificial Intelligence, University of Toronto Institute for Aerospace Studies (UTIAS), Toronto, ON, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4438-5876","authenticated-orcid":false,"given":"Abhishek","family":"Goudar","sequence":"additional","affiliation":[{"name":"The University of Toronto Robotics Institute, Vector Institute for Artificial Intelligence, University of Toronto Institute for Aerospace Studies (UTIAS), Toronto, ON, Canada"}]},{"given":"Xinyuan","family":"Qiao","sequence":"additional","affiliation":[{"name":"The University of Toronto Robotics Institute, Vector Institute for Artificial Intelligence, University of Toronto Institute for Aerospace Studies (UTIAS), Toronto, ON, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4012-4668","authenticated-orcid":false,"given":"Angela P.","family":"Schoellig","sequence":"additional","affiliation":[{"name":"The University of Toronto Robotics Institute, Vector Institute for Artificial Intelligence, University of Toronto Institute for Aerospace Studies (UTIAS), Toronto, ON, Canada"},{"name":"Munich Institute for Robotics and Machine Intelligence (MIRMI), Technical University of Munich, Munich, Germany"}]}],"member":"179","published-online":{"date-parts":[[2024,2,5]]},"reference":[{"key":"bibr1-02783649241230640","unstructured":"Adidas (2022) Adidas reveals the first FIFA world cup official match ball featureing connected ball technology. 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