DANAE：用于水下姿态估计的去噪自编码器
DANAE: a denoising autoencoder for underwater attitude estimation
AI总结：
本文提出 DANAE，一种作用于 Kalman 滤波 IMU/AHRS 数据的深度去噪自编码器，以在不区分噪声类型的情况下提升水下机器人姿态估计的稳健性与精度。
摘要：
水下机器人导航的主要问题之一是其精确定位，而这在很大程度上取决于方位估计阶段。为此采用的系统会受到不同类型噪声的影响，这些噪声主要与传感器以及水下环境的不规则噪声有关。如果配置得当，滤波算法可以减弱其影响，但这一过程通常需要精细的技术和时间。本文提出 DANAE，这是一种用于姿态估计的深度去噪自编码器（Denoising AutoeNcoder for Attitude Estimation），它对 Kalman 滤波器的 IMU/AHRS 数据融合结果进行处理，旨在减弱任何类型的噪声，而不论其性质如何。这种基于深度学习的架构表现出稳健性和可靠性，显著改善了 Kalman 滤波器的结果。进一步的测试可能使该方法适用于导航任务中的实时应用。
英文摘要：
One of the main issues for underwater robots navigation is their accurate positioning, which heavily depends on the orientation estimation phase. The systems employed to this scope are affected by different noise typologies, mainly related to the sensors and to the irregular noise of the underwater environment. Filtering algorithms can reduce their effect if opportunely configured, but this process usually requires fine techniques and time. In this paper we propose DANAE, a deep Denoising AutoeNcoder for Attitude Estimation which works on Kalman filter IMU/AHRS data integration with the aim of reducing any kind of noise, independently of its nature. This deep learning-based architecture showed to be robust and reliable, significantly improving the Kalman filter results. Further tests could make this method suitable for real-time applications on navigation tasks.
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