IONet: Learning to Cure the Curse of Drift in Inertial Odometry

Changhao Chen, Xiaoxuan Lu, Andrew Markham, Niki Trigoni

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Inertial sensors play a pivotal role in indoor localization, which in turn lays the foundation for pervasive personal applications. However, low-cost inertial sensors, as commonly found in smartphones, are plagued by bias and noise, which leads to unbounded growth in error when accelerations are double integrated to obtain displacement. Small errors in state estimation propagate to make odometry virtually unusable in a matter of seconds. We propose to break the cycle of continuous integration, and instead segment inertial data into independent windows. The challenge becomes estimating the latent states of each window, such as velocity and orientation, as these are not directly observable from sensor data. We demonstrate how to formulate this as an optimization problem, and show how deep recurrent neural networks can yield highly accurate trajectories, outperforming state-of-the-art shallow techniques, on a wide range of tests and attachments. In particular, we demonstrate that IONet can generalize to estimate odometry for non-periodic motion, such as a shopping trolley or baby-stroller, an extremely challenging task for existing techniques.
Original languageEnglish
Title of host publicationThe Thirty-Second AAAI Conferenceon Artificial Intelligence (AAAI-18)
PublisherAAAI Press
Pages6468-6476
Number of pages9
ISBN (Electronic)978-1-57735-800-8
Publication statusPublished - 7 Feb 2018
EventThirty-Second AAAI Conference on Artificial Intelligence - Hilton New Orleans Riverside, New Orleans, United States
Duration: 2 Feb 20187 Feb 2018
https://aaai.org/Conferences/AAAI-18/
https://aaai.org/Conferences/AAAI-18/

Publication series

Name
PublisherAAAI Press
ISSN (Electronic)2374-3468

Conference

ConferenceThirty-Second AAAI Conference on Artificial Intelligence
Abbreviated titleAAAI 2018
CountryUnited States
CityNew Orleans
Period2/02/187/02/18
Internet address

Keywords

  • Indoor Localization
  • Inertial navigation
  • Deep Neural Networks

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