Imaging sonar simulator for assessment of image registration techniques

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

doi:10.23919/oceans40490.2019.8962864

Cited by 6 publications

(7 citation statements)

References 24 publications

(25 reference statements)

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“…The loss function in the regression layer is the Mean Squared Error (MSE). The sonar simulator described in [38] uses ray-tracing to generate sonar images. The underwater vehicles and sonar sensors are implemented in the development platform Unity [39].…”

Section: A DL Network Architecture Using Sonar Images As the Inputmentioning

confidence: 99%

Sonar FoV Segmentation for Motion Estimation Using DL Networks

2022

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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.

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Section: A DL Network Architecture Using Sonar Images As the Inputmentioning

confidence: 99%

Sonar FoV Segmentation for Motion Estimation Using DL Networks

2022

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“…The simulator is implemented in Unreal Engine to display the simulated environment and ROS to manipulate the platforms. The sonar simulator presented in [32] is capable of simulating sonars that can be used for validating image registration techniques. The simulated sonar sensors and the underwater environment are implemented in Unity [33].…”

Section: Simulator To Generate Impulse Responses Of the Acoustic Envi...mentioning

confidence: 99%

Motion Estimation of Underwater Platforms Using Impulse Responses From the Seafloor

et al. 2022

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Autonomous underwater vehicles require accurate navigation. Techniques such as image registration using consecutive acoustic images from a sonar have shown promising results for this task. The implementation of such techniques using sonar images augmented with deep learning (DL) networks demonstrate high navigation accuracy; this is possible even with highly compressed images. The sonar images are estimates of sampled in time (with a ping period) magnitudes of channel impulse responses representing the underwater acoustic environment. More information about the environment is contained in (almost) continuous in time estimates of the channel impulse responses. Such estimates can be obtained using full-duplex technology. Rather than using sonar images, this paper investigates the use of channel impulse response estimates for underwater platform motion estimation. The proposed system uses a single projector and a small number of receiving transducers installed on the moving platform. A DL network is used to estimate the motion in two degrees of freedom (forward/backward and sideways), using two or more consecutive impulse response estimates as the input. To train the DL network, a specially designed simulator is used to model the underwater acoustic environment, populated with multiple objects spread on the seafloor. The proposed technique can significantly reduce the acoustic hardware and processing complexity of the DL network and obtain a higher accuracy of motion estimation, compared with techniques based on the processing of sonar images, e.g., the error achieved with the technique proposed in this paper is 1.7% of the maximum platform displacement, compared to 4% achieved with a technique using sonar images. The navigation accuracy is further illustrated by examples of estimation of complex trajectories.

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“…The sonar simulator proposed in [16] and used in [14] for training DL networks, is built upon the development software Unity [17]. It is based on a ray-tracing technique to generate the images.…”

Section: Sonar Simulatormentioning

confidence: 99%

“…The simulator in [16] only uses a single sonar sensor with the same transmit-receive antenna. We expanded the sonar simulator to separate the sonar transmitter from the receiver.…”

Section: Sonar Simulatormentioning

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%

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

Almanza-Medina¹,

Henson²,

Zakharov³

2021

Preprint

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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.

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Imaging sonar simulator for assessment of image registration techniques

Cited by 6 publications

References 24 publications

Sonar FoV Segmentation for Motion Estimation Using DL Networks

Sonar FoV Segmentation for Motion Estimation Using DL Networks

Motion Estimation of Underwater Platforms Using Impulse Responses From the Seafloor

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

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