DANAE: a denoising autoencoder for underwater attitude estimation
This addresses the challenge of noise reduction in underwater robot navigation, offering a solution that could enhance positioning accuracy, though it appears incremental as it builds on existing Kalman filter methods.
The paper tackles the problem of accurate orientation estimation for underwater robots, which is affected by various noise types, by proposing DANAE, a deep denoising autoencoder that works on Kalman filter IMU/AHRS data. The method significantly improves Kalman filter results, showing robustness and reliability, with potential for real-time navigation applications.
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.