  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
https://github.com/mbrossar/denoise-imu-gyro.
