论文标题
用深度学习进行开环态度估计的imu陀螺仪
Denoising IMU Gyroscopes with Deep Learning for Open-Loop Attitude Estimation
论文作者
论文摘要
本文提出了一种学习方法，用于使用地面真实数据来降低惯性测量单元（IMU）的陀螺仪，并实时估算机器人在死去的估算中的方向（态度）。获得的算法优于（看不见的）测试序列上的最新算法。得益于精心挑选的模型，用于方向增量的适当损失函数以及通过高频惯性数据训练时的关键点，可以实现获得的性能。我们的方法基于基于扩张的卷积的神经网络，而无需任何复发的神经网络。我们证明了对EUROC和TUM-VI数据集的3D态度估计的策略效率有多么有效。有趣的是，我们观察到死者的算法算法设法通过态度估计来击败排名排名的视觉惯性遗漏系统，尽管它不使用视觉传感器。我们认为，本文为视觉惯性定位提供了新的观点，并构成了迈出涉及IMU的更有效学习方法的一步。我们的开源实施可在https://github.com/mbrossar/denoise-imu-gyro中获得。
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.
