{"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/ridi-robust-imu-double-integration","title":"RIDI: Robust IMU Double Integration","arxiv_id":"1712.09004","date":"2017-12-25","proceeding":"ECCV 2018 9","authors":["Hang Yan","Qi Shan","Yasutaka Furukawa"],"abstract":"This paper proposes a novel data-driven approach for inertial navigation,\nwhich learns to estimate trajectories of natural human motions just from an\ninertial measurement unit (IMU) in every smartphone. The key observation is\nthat human motions are repetitive and consist of a few major modes (e.g.,\nstanding, walking, or turning). Our algorithm regresses a velocity vector from\nthe history of linear accelerations and angular velocities, then corrects\nlow-frequency bias in the linear accelerations, which are integrated twice to\nestimate positions. We have acquired training data with ground-truth motions\nacross multiple human subjects and multiple phone placements (e.g., in a bag or\na hand). The qualitatively and quantitatively evaluations have demonstrated\nthat our algorithm has surprisingly shown comparable results to full Visual\nInertial navigation. To our knowledge, this paper is the first to integrate\nsophisticated machine learning techniques with inertial navigation, potentially\nopening up a new line of research in the domain of data-driven inertial\nnavigation. We will publicly share our code and data to facilitate further\nresearch.","url_abs":"http://arxiv.org/abs/1712.09004v2","url_pdf":"http://arxiv.org/pdf/1712.09004v2.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":"abstracts"},"code_links":[{"paper_slug":"ridi-robust-imu-double-integration","repo_url":"https://github.com/higerra/ridi_imu","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":"https://app.syntology.ai/?focus=1712.09004","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}