A Fast and Robust Algorithm for Orientation Estimation using Inertial Sensors

Manon Kok, Thomas B. Schön

Research output: Contribution to journalArticleScientificpeer-review

18 Citations (Scopus)
39 Downloads (Pure)


We present a novel algorithm for online, real-time orientation estimation. Our algorithm integrates gyroscope data and corrects the resulting orientation estimate for integration drift using accelerometer and magnetometer data. This correction is computed, at each time instance, using a single gradient descent step with fixed step length. This fixed step length results in robustness against model errors, e.g., caused by large accelerations or by short-term magnetic field disturbances, which we numerically illustrate using Monte Carlo simulations. Our algorithm estimates a three-dimensional update to the orientation rather than the entire orientation itself. This reduces the computational complexity by approximately 1/3 with respect to the state of the art. It also improves the quality of the resulting estimates, specifically when the orientation corrections are large. We illustrate the efficacy of the algorithm using experimental data.
Original languageEnglish
Pages (from-to)1673-1677
JournalIEEE Signal Processing Letters
Issue number11
Publication statusPublished - 2019

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.


  • Orientation estimation
  • inertial sensors
  • complementary filter
  • multiplicative extended Kalman filter


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