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

• URL: http://arxiv.org/abs/2002.10718v2
• Date: Fri, 26 Jun 2020 07:43:00 GMT
• Title: Denoising IMU Gyroscopes with Deep Learning for Open-Loop Attitude Estimation
• Authors: Martin Brossard (CAOR), Silvere Bonnabel (UNC), Axel Barrau (CAOR)
• Abstract summary: This paper proposes a learning method for denoising gyroscopes of Inertial Measurement Units (IMUs) using ground truth data. The obtained algorithm outperforms the state-of-the-art on the (unseen) test sequences.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 https://github.com/mbrossar/denoise-imu-gyro.

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