深層学習によるIMUジャイロのノイズ除去と開ループ姿勢推定
Denoising IMU Gyroscopes with Deep Learning for Open-Loop Attitude Estimation
拡張畳み込みニューラルネットワークを用いてIMUジャイロのノイズを除去し、デッドレコニングでリアルタイムに3D姿勢を推定する手法を提案した論文。
著者: Martin Brossard, Silvere Bonnabel, Axel Barrau
分類: cs.RO, stat.ML
原文アブストラクト
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.
