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Denoising IMU Gyroscopes with Deep Learning for Open-Loop Attitude Estimation

Abstract : This paper proposes a learning method for denoising gyroscopes of Inertial Measurement Units (IMUs) using ground truth data, and estimating in real time the orientation (attitude) of a robot in dead reckoning. The obtained algorithm outperforms the state-of-the-art on the (unseen) test sequences. The obtained performances are achieved thanks to a well-chosen model, a proper loss function for orientation increments, and through the identification of key points when training with high-frequency inertial data. Our approach builds upon a neural network based on dilated convolutions, without requiring any recurrent neural network. We demonstrate how efficient our strategy is for 3D attitude estimation on the EuRoC and TUM-VI datasets. Interestingly, we observe our dead reckoning algorithm manages to beat top-ranked visual-inertial odometry systems in terms of attitude estimation although it does not use vision sensors. We believe this paper offers new perspectives for visual-inertial localization and constitutes a step toward more efficient learning methods involving IMUs. Our open-source implementation is available at
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Submitted on : Thursday, July 2, 2020 - 10:08:47 AM
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  • HAL Id : hal-02488923, version 3
  • ARXIV : 2002.10718


Martin Brossard, Silvere Bonnabel, Axel Barrau. Denoising IMU Gyroscopes with Deep Learning for Open-Loop Attitude Estimation. IEEE Robotics and Automation Letters, IEEE In press. ⟨hal-02488923v3⟩



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