论文标题
DANAE：水下态度估计的Denoing AutoCododer
DANAE: a denoising autoencoder for underwater attitude estimation
论文作者
论文摘要
水下机器人导航的主要问题之一是它们的准确定位，这在很大程度上取决于方向估计阶段。该范围所采用的系统受不同的噪声类型的影响，主要与传感器和水下环境的不规则噪声有关。过滤算法可以及时地配置，可以降低其效果，但是此过程通常需要精细的技术和时间。在本文中，我们提出了DANAE，这是一种对态度估计的深度降级自动编码器，可用于Kalman Filter IMU/AHRS数据集成，目的是减少任何类型的噪声，而与其性质无关。这种基于深度学习的体系结构表现出强大而可靠的，可显着改善卡尔曼过滤器的结果。进一步的测试可以使此方法适用于导航任务的实时应用程序。
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.
