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 learningbased 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.

DANAE: A denoising autoencoder for underwater attitude estimation / Russo, Paolo; Di Ciaccio, Fabiana; Troisi, Salvatore. - (2020), pp. 213-217. (Intervento presentato al convegno IMEKO TC19 International Workshop on Metrology for the Sea, MetroSea 2020 tenutosi a Napoli, Italy).

DANAE: A denoising autoencoder for underwater attitude estimation

Russo Paolo
Primo
Investigation
;
2020

Abstract

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 learningbased 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.
2020
IMEKO TC19 International Workshop on Metrology for the Sea, MetroSea 2020
Data integration; Learning systems; Robots; Attitude estimation; Auto encoders; De-noising; Filtering algorithm; Navigation tasks; Orientation estimation; Real-time application; Robot navigation; Underwater environments; Underwater robots; Kalman filters
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
DANAE: A denoising autoencoder for underwater attitude estimation / Russo, Paolo; Di Ciaccio, Fabiana; Troisi, Salvatore. - (2020), pp. 213-217. (Intervento presentato al convegno IMEKO TC19 International Workshop on Metrology for the Sea, MetroSea 2020 tenutosi a Napoli, Italy).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1658854
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