{"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/inertial-odometry-on-handheld-smartphones","title":"Inertial Odometry on Handheld Smartphones","arxiv_id":"1703.00154","date":"2017-03-01","proceeding":null,"authors":["Arno Solin","Santiago Cortes","Esa Rahtu","Juho Kannala"],"abstract":"Building a complete inertial navigation system using the limited quality data\nprovided by current smartphones has been regarded challenging, if not\nimpossible. This paper shows that by careful crafting and accounting for the\nweak information in the sensor samples, smartphones are capable of pure\ninertial navigation. We present a probabilistic approach for orientation and\nuse-case free inertial odometry, which is based on double-integrating rotated\naccelerations. The strength of the model is in learning additive and\nmultiplicative IMU biases online. We are able to track the phone position,\nvelocity, and pose in real-time and in a computationally lightweight fashion by\nsolving the inference with an extended Kalman filter. The information fusion is\ncompleted with zero-velocity updates (if the phone remains stationary),\naltitude correction from barometric pressure readings (if available), and\npseudo-updates constraining the momentary speed. We demonstrate our approach\nusing an iPad and iPhone in several indoor dead-reckoning applications and in a\nmeasurement tool setup.","url_abs":"http://arxiv.org/abs/1703.00154v2","url_pdf":"http://arxiv.org/pdf/1703.00154v2.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":"inertial-odometry-on-handheld-smartphones","repo_url":"https://github.com/dmckinnon/mapper","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=1703.00154","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}