Deep Learning Architectures for Navigation Using Forward Looking Sonar Images

Almanza-Medina, Jose E.; Henson, Benjamin; Zakharov, Yuriy

doi:10.1109/access.2021.3061440

Cited by 11 publications

(36 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, due to its complexity, this method is difficult to implement in real-time. In [14], we presented a method based on DL networks to estimate the motion of an underwater Fig. 1: Sonar FoV parameters and the coordinate system relative to the sonar.…”

Section: Introductionmentioning

confidence: 99%

“…The method significantly reduces the complexity and processing time compared to the method in [13]. The low processing time makes the methods in [14] suitable for real-time applications. The DL networks in [14] allow a millimeter accuracy in positioning between two sonar images.…”

Section: Introductionmentioning

confidence: 99%

“…The low processing time makes the methods in [14] suitable for real-time applications. The DL networks in [14] allow a millimeter accuracy in positioning between two sonar images. However, higher estimation accuracy is required for some applications, e.g., synthetic aperture sonars [15].…”

Section: Introductionmentioning

confidence: 99%

“…However, higher estimation accuracy is required for some applications, e.g., synthetic aperture sonars [15]. In [14], a single sonar sensor was considered for motion estimation. The purpose of this paper is to consider the use of two sensors separated from the sonar transmitter to find out how it can improve the accuracy even further.…”

Section: Introductionmentioning

confidence: 99%

“…The use of the DL approach has the problem of acquiring big volumes of labeled data for training the networks. In [14], synthetic images are generated by a sonar simulator from [16] to solve this problem. In this work, we modify the sonar simulator from [16] to allow acoustic images to be generated for more complicated sonar configurations and use these images for training and validation of DL networks.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Motion Estimation of an Underwater Platform Using Images From Two Sonar Sensors

Almanza-Medina¹,

Henson²,

Zakharov³

2021

Preprint

Self Cite

View full text Add to dashboard Cite

Many underwater applications that involve the use of autonomous underwater vehicles require accurate navigation systems. Image registration from acoustic images is a technique that can be used to achieve this task by comparing two consecutive sonar images and estimate the motion of the vechicle. The use of deep learning (DL) techniques for motion estimation can significantly reduce the processing complexity and achieve high-accuracy position estimates. In this paper we investigate the performance improvement when using two sonar sensors compared to using a single sensor. The DL network is trained using images generated by a sonar simulator. The results show an improvement in the estimation accuracy when using two sensors.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Motion Estimation of an Underwater Platform Using Images From Two Sonar Sensors

Almanza-Medina¹,

Henson²,

Zakharov³

2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Generative Adversarial Super-Resolution at the edge with knowledge distillation

Angarano

Salvetti

Martini

et al. 2023

Engineering Applications of Artificial Intelligence

View full text Add to dashboard Cite

Sonar FoV Segmentation for Motion Estimation Using DL Networks

2022

Self Cite

View full text Add to dashboard Cite

Accurate navigation of an autonomous underwater vehicle is important for its reliable operation. However, this task is challenging due to limitations of radio wave propagation and poor visibility in the aquatic environment. Underwater navigation techniques based on analysing sonar images facilitated by machine learning have shown promising results. However, previously proposed techniques are still complicated for real-time applications. This paper investigates low complexity techniques for the motion estimation based on the use of images obtained by a sonar looking down to the seafloor. The sonar can use multiple beams within a field of view (FoV). Various configurations of beams are considered according to portions of the FoV covered and two estimation approaches are investigated. In one approach, the sonar images are directly processed by a deep learning (DL) network, whereas in the other, the images are converted into (reduced size) vectors before applying them to a DL network. The vector approach shows a significantly lower computation time (about 10 times faster), which makes it suitable for realtime applications. Both the approaches show a similar estimation accuracy, about 10% of the maximum magnitude of the motion. The vector technique has been used to estimate a simulated trajectory and compare the estimate with the ground truth, which showed a good match. It has also been applied to estimate the trajectory of an imaging sonar from a real data set from a ship's hull inspection. The estimated trajectory has successfully been used to build a mosaic by merging the sonar images from the real data set.

show abstract

Deep Learning Architectures for Navigation Using Forward Looking Sonar Images

Cited by 11 publications

References 44 publications

Motion Estimation of an Underwater Platform Using Images From Two Sonar Sensors

Motion Estimation of an Underwater Platform Using Images From Two Sonar Sensors

Generative Adversarial Super-Resolution at the edge with knowledge distillation

Sonar FoV Segmentation for Motion Estimation Using DL Networks

Contact Info

Product

Resources

About