English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

One of the main issues for underwater robots navigation is their accuratepositioning, which heavily depends on the orientation estimation phase. Thesystems employed to this scope are affected by different noise typologies,mainly related to the sensors and to the irregular noise of the underwaterenvironment. Filtering algorithms can reduce their effect if opportunelyconfigured, but this process usually requires fine techniques and time. In thispaper we propose DANAE, a deep Denoising AutoeNcoder for Attitude Estimationwhich works on Kalman filter IMU/AHRS data integration with the aim of reducingany kind of noise, independently of its nature. This deep learning-basedarchitecture showed to be robust and reliable, significantly improving theKalman filter results. Further tests could make this method suitable forreal-time applications on navigation tasks.

DANAE: a denoising autoencoder for underwater attitude estimation