{"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/deep-learning-based-speed-estimation-for","title":"Deep Learning Based Speed Estimation for Constraining Strapdown Inertial Navigation on Smartphones","arxiv_id":"1808.03485","date":"2018-08-10","proceeding":null,"authors":["Santiago Cortés","Arno Solin","Juho Kannala"],"abstract":"Strapdown inertial navigation systems are sensitive to the quality of the\ndata provided by the accelerometer and gyroscope. Low-grade IMUs in handheld\nsmart-devices pose a problem for inertial odometry on these devices. We propose\na scheme for constraining the inertial odometry problem by complementing\nnon-linear state estimation by a CNN-based deep-learning model for inferring\nthe momentary speed based on a window of IMU samples. We show the feasibility\nof the model using a wide range of data from an iPhone, and present\nproof-of-concept results for how the model can be combined with an inertial\nnavigation system for three-dimensional inertial navigation.","url_abs":"http://arxiv.org/abs/1808.03485v1","url_pdf":"http://arxiv.org/pdf/1808.03485v1.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":"deep-learning-based-speed-estimation-for","repo_url":"https://github.com/AaltoVision/deep-speed-constrained-ins","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"state-estimation","task_name":"State Estimation"}],"methods":[{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1808.03485","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}