No abstract

This work focuses on building a Forward-Looking sonar simulator capable of generating large volumes of ground truth data to test algorithms such as: novel image registration techniques for trajectory estimation, three-dimensional reconstruction or to be used as training data for machine learning algorithms. The simulator is capable of generating realistic data sets of images and providing ground truth data with the exact position and attitude of the sonar related to objects in a test scenario. The sonar simulator is developed using the Unity software platform. This work also shows an example application. Simulated image data sets for different sonar trajectories and seabed textures were created. These data sets are used by an attitude-trajectory estimation method and a quantitative analysis of the method is presented.

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.

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.

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.