DANAE++: A Smart Approach for Denoising Underwater Attitude Estimation

One of the main issues for the navigation of underwater robots consists in accurate vehicle positioning, which heavily depends on the orientation estimation phase. The systems employed to this end 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. This paper presents DANAE++, an improved denoising autoencoder based on DANAE (deep Denoising AutoeNcoder for Attitude Estimation), which is able to recover Kalman Filter (KF) IMU/AHRS orientation estimations from any kind of noise, independently of its nature. This deep learning-based architecture already proved to be robust and reliable, but in its enhanced implementation significant improvements are obtained in terms of both results and performance. In fact, DANAE++ is able to denoise the three angles describing the attitude at the same time, and that is verified also using the estimations provided by an extended KF. Further tests could make this method suitable for real-time applications in navigation tasks.

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DANAE++: A Smart Approach for Denoising Underwater Attitude Estimation

10.20944/preprints202101.0344.v1 ◽

2021 ◽

Author(s):

Paolo Russo ◽

Fabiana Di Ciaccio ◽

Salvatore Troisi

Keyword(s):

Deep Learning ◽

Kalman Filter ◽

Real Time ◽

Denoising Autoencoder ◽

Orientation Estimation ◽

Underwater Robots ◽

Underwater Environment ◽

Real Time Applications ◽

And Performance ◽

Vehicle Positioning

One of the main issues for underwater robots navigation is represented by the accurate vehicle 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. This paper presents DANAE++, an improved denoising autoencoder based on DANAE, which is able to recover Kalman Filter IMU/AHRS orientation estimations from any kind of noise, independently of its nature. This deep learning-based architecture already proved to be robust and reliable, but in its enhanced implementation significant improvements are obtained both in terms of results and performance. In fact, DANAE++is able to denoise the three angles describing the attitude at the same time, and that is verified also on the estimations provided by the more performing Extended KF. Further tests could make this method suitable for real-time applications on navigation tasks.

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Concepts and Real-Time Applications of Deep Learning

10.1007/978-3-030-76167-7 ◽

2021 ◽

Keyword(s):

Deep Learning ◽

Real Time ◽

Real Time Applications

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Real-Time Attitude Estimation of Sigma-Point Kalman Filter via Matrix Operation Accelerator

2019 IEEE 13th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC) ◽

10.1109/mcsoc.2019.00055 ◽

2019 ◽

Author(s):

Zeyang Dai ◽

Lei Jing

Keyword(s):

Kalman Filter ◽

Real Time ◽

Attitude Estimation ◽

Matrix Operation ◽

Sigma Point

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Real-Time Vehicle Make and Model Recognition with the Residual SqueezeNet Architecture

Sensors ◽

10.3390/s19050982 ◽

2019 ◽

Vol 19 (5) ◽

pp. 982 ◽

Cited By ~ 9

Author(s):

Hyo Lee ◽

Ihsan Ullah ◽

Weiguo Wan ◽

Yongbin Gao ◽

Zhijun Fang

Keyword(s):

Deep Learning ◽

Real Time ◽

Large Scale ◽

Recognition Rate ◽

Experimental Results ◽

Learning Approach ◽

Deep Model ◽

Proposed Model ◽

Real Time Applications ◽

Model Recognition

Make and model recognition (MMR) of vehicles plays an important role in automatic vision-based systems. This paper proposes a novel deep learning approach for MMR using the SqueezeNet architecture. The frontal views of vehicle images are first extracted and fed into a deep network for training and testing. The SqueezeNet architecture with bypass connections between the Fire modules, a variant of the vanilla SqueezeNet, is employed for this study, which makes our MMR system more efficient. The experimental results on our collected large-scale vehicle datasets indicate that the proposed model achieves 96.3% recognition rate at the rank-1 level with an economical time slice of 108.8 ms. For inference tasks, the deployed deep model requires less than 5 MB of space and thus has a great viability in real-time applications.

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