Underwater Motion Estimation Based on Acoustic Images and Deep Learning

This work develops techniques to estimate the motion of an underwater vehicle by processing acoustic images using deep learning (DL). For this, an underwater sonar simulator based on ray-tracing is designed and implemented. The simulator provides the ground truth data to train and validate proposed techniques. Several DL networks are implemented and compared to identify the most suitable for motion estimation using sonar images. The DL methods showed a much lower computation time and more accurate motion estimates compared to a deterministic algorithm. Further improvements of the DL methods are investigated by preprocessing the data before feeding it to the DL network. One technique converts sonar images into vectors by adding up the pixels in each row. This reduces the size of the DL networks. This technique showed significant reduction in the computation time of up to 10 times compared to techniques that use images. Another preprocessing technique divides the field of view (FoV) of a simulated sonar into four quadrants. An image is generated from each quadrant. This is combined with the vector technique by converting the images into vectors and grouping them together as the input of the DL network. The FoV division approach showed a high accuracy compared to using the whole FoV or different portions of it. Another motion estimation method presented in this work is enabled by full-duplex operation and rather than using images, it is based on DL analysis of time variation of complex-valued channel impulse responses. This technique can significantly reduce the acoustic hardware and processing complexity of the DL network and obtain a higher motion estimation accuracy, compared with techniques based on the processing of sonar images. The navigation accuracy of all the techniques is further illustrated by examples of estimation of complex trajectories using simulated and real data

Advances in Sonar Technology

The demand to explore the largest and also one of the richest parts of our planet, the advances in signal processing promoted by an exponential growth in computation power and a thorough study of sound propagation in the underwater realm, have lead to remarkable advances in sonar technology in the last years.The work on hand is a sum of knowledge of several authors who contributed in various aspects of sonar technology. This book intends to give a broad overview of the advances in sonar technology of the last years that resulted from the research effort of the authors in both sonar systems and their applications. It is intended for scientist and engineers from a variety of backgrounds and even those that never had contact with sonar technology before will find an easy introduction with the topics and principles exposed here

3D reconstruction and object recognition from 2D SONAR data

Accurate and meaningful representations of the environment are required for autonomy in underwater applications. Thanks to favourable propagation properties in water, acoustic sensors are commonly preferred to video cameras and lasers but do not provide direct 3D information. This thesis addresses the 3D reconstruction of underwater scenes from 2D imaging SONAR data as well as the recognition of objects of interest in the reconstructed scene. We present two 3D reconstruction methods and two model-based object recognition methods. We evaluate our algorithms on multiple scenarios including data gathered by an AUV. We show the ability to reconstruct underwater environments at centimetre-level accuracy using 2D SONARs of any aperture. We demonstrate the recognition of structures of interest on a medium-sized oil-field type environment providing accurate yet low memory footprint semantic world models. We conclude that accurate 3D semantic representations of partially-structured marine environments can be obtained from commonly embedded 2D SONARs, enabling online world modelling, relocalisation and model-based applications

Interferometric synthetic aperture sonar system supported by satellite

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200

Towards autonomous localization and mapping of AUVs: a survey

Purpose The main purpose of this paper is to investigate two key elements of localization and mapping of Autonomous Underwater Vehicle (AUV), i.e. to overview various sensors and algorithms used for underwater localization and mapping, and to make suggestions for future research. Design/methodology/approach The authors first review various sensors and algorithms used for AUVs in the terms of basic working principle, characters, their advantages and disadvantages. The statistical analysis is carried out by studying 35 AUV platforms according to the application circumstances of sensors and algorithms. Findings As real-world applications have different requirements and specifications, it is necessary to select the most appropriate one by balancing various factors such as accuracy, cost, size, etc. Although highly accurate localization and mapping in an underwater environment is very difficult, more and more accurate and robust navigation solutions will be achieved with the development of both sensors and algorithms. Research limitations/implications This paper provides an overview of the state of art underwater localisation and mapping algorithms and systems. No experiments are conducted for verification. Practical implications The paper will give readers a clear guideline to find suitable underwater localisation and mapping algorithms and systems for their practical applications in hand. Social implications There is a wide range of audiences who will benefit from reading this comprehensive survey of autonomous localisation and mapping of UAVs. Originality/value The paper will provide useful information and suggestions to research students, engineers and scientists who work in the field of autonomous underwater vehicles

EM 2000 Microbathymetric and HYDROSWEEP DS-2 Bathymetric Surveying – a Comparison of Seafloor Topography at Porcupine Bank, west of Ireland

One of the latest discoveries in the world oceans are carbonate structures in the North-East Atlantic. In the frameworks of several European projects, the research vessel POLARSTERN and underwater robot VICTOR 6000 were engaged to explore these areas. The data described in this thesis were collected during the expedition ARK XIX/3 between 16 - 19th June 2003. Bathymetric and microbathymetric data in parts of the Pelagia Province, located on the northern Porcupine Bank, west of Ireland, were measured with two multibeam sonar systems deployed at different distances from the bottom. The four compared models come from a KONGSBERG SIMRAD EM 2000 multibeam sonar system and an ATLAS ELEKTRONIK HYDROSWEEP DS-2 multibeam sonar system. After necessary corrections of the data, digital terrain models were created, subtracted and correlated using appropriate software. This thesis begins with a description of the historical background of bathymetry, followed by a description of the principles of navigation and underwater navigation, inertial navigation systems, and the calibration of these systems. Systematic errors will be pointed out. It examines the measurement principles of the echo sounders used on the ARK XIX/3a expedition and accompanying necessary procedures, such as CTD measurements. A discussion of how the data are processed from raw data to edited results, and the effects of the errors, follows. One chapter is dedicated to a comparison and interpretation of the data. Sidescan, mosaic and PARASOUND data from the Hedge and Scarp Mounds are introduced as complementary information

3D reconstruction and motion estimation using forward looking sonar

Autonomous Underwater Vehicles (AUVs) are increasingly used in different domains including archaeology, oil and gas industry, coral reef monitoring, harbour’s security, and mine countermeasure missions. As electromagnetic signals do not penetrate underwater environment, GPS signals cannot be used for AUV navigation, and optical cameras have very short range underwater which limits their use in most underwater environments. Motion estimation for AUVs is a critical requirement for successful vehicle recovery and meaningful data collection. Classical inertial sensors, usually used for AUV motion estimation, suffer from large drift error. On the other hand, accurate inertial sensors are very expensive which limits their deployment to costly AUVs. Furthermore, acoustic positioning systems (APS) used for AUV navigation require costly installation and calibration. Moreover, they have poor performance in terms of the inferred resolution. Underwater 3D imaging is another challenge in AUV industry as 3D information is increasingly demanded to accomplish different AUV missions. Different systems have been proposed for underwater 3D imaging, such as planar-array sonar and T-configured 3D sonar. While the former features good resolution in general, it is very expensive and requires huge computational power, the later is cheaper implementation but requires long time for full 3D scan even in short ranges. In this thesis, we aim to tackle AUV motion estimation and underwater 3D imaging by proposing relatively affordable methodologies and study different parameters affecting their performance. We introduce a new motion estimation framework for AUVs which relies on the successive acoustic images to infer AUV ego-motion. Also, we propose an Acoustic Stereo Imaging (ASI) system for underwater 3D reconstruction based on forward looking sonars; the proposed system features cheaper implementation than planar array sonars and solves the delay problem in T configured 3D sonars

Toward autonomous harbor surveillance

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Includes bibliographical references (p. 105-113).In this thesis we address the problem of drift-free navigation for underwater vehicles performing harbor surveillance and ship hull inspection. Maintaining accurate localization for the duration of a mission is important for a variety of tasks, such as planning the vehicle trajectory and ensuring coverage of the area to be inspected. Our approach uses only onboard sensors in a simultaneous localization and mapping setting and removes the need for any external infrastructure like acoustic beacons. We extract dense features from a forward-looking imaging sonar and apply pair-wise registration between sonar frames. The registrations are combined with onboard velocity, attitude and acceleration sensors to obtain an improved estimate of the vehicle trajectory. In addition, an architecture for a persistent mapping is proposed. With the intention of handling long term operations and repetitive surveillance tasks. The proposed architecture is flexible and supports different types of vehicles and mapping methods. The design of the system is demonstrated with an implementation of some of the key features of the system. In addition, methods for re-localization are considered. Finally, results from several experiments that demonstrate drift-free navigation in various underwater environments are presented.by Hordur Johannsson.S.M

Dense, sonar-based reconstruction of underwater scenes

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Mechanical Engineering at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution September 2019.Three-dimensional maps of underwater scenes are critical to—or the desired end product of—many applications, spanning a spectrum of spatial scales. Examples range from inspection of subsea infrastructure to hydrographic surveys of coastlines. Depending on the end use, maps will have different accuracy requirements. The accuracy of a mapping platform depends mainly on the individual accuracies of (i) its pose estimate in some global frame, (ii) the estimates of offsets between mapping sensors and platform, and (iii) the accuracy of the mapping sensor measurements. Typically, surface-based surveying platforms will employ highly accurate positioning sensors—e.g. a combination of differential global navigation satellite system (GNSS) receiver with an accurate attitude and heading reference system—to instrument the pose of a mapping sensor such as a multibeam sonar. For underwater platforms, the rapid attenuation of electromagnetic signals in water precludes the use of GNSS receivers at any meaningful depth. Acoustic positioning systems, the underwater analogues to GNSS, are limited to small survey areas and free of obstacles that may result in undesirable acoustic effects such as multi-path propagation and reverberation. Save for a few exceptions, the accuracy and update rate of these systems is significantly lower than that of differential GNSS. This performance reduction shifts the accuracy burden to inertial navigation systems (INS), often aided by Doppler velocity logs. Still, the pose estimates of an aided INS will incur in unbounded drift growth over time, often necessitating the use of techniques such as simultaneous localization and mapping (SLAM) to leverage local features to bound the uncertainty in the position estimate. The contributions presented in this dissertation aim at improving the accuracy of maps of underwater scenes produced from multibeam sonar data. First, we propose robust methods to process and segment sonar data to obtain accurate range measurements in the presence of noise, sensor artifacts, and outliers. Second, we propose a volumetric, submap-based SLAM technique that can successfully leverage map information to correct for drift in the mapping platform’s pose estimate. Third, and informed by the previous two contributions, we propose a dense approach to the sonar-based reconstruction problem, in which the pose estimation, sonar segmentation and model optimization problems are tackled simultaneously under the unified framework of factor graphs. This stands in contrast with the traditional approach where the sensor processing and segmentation, pose estimation, and model reconstruction problems are solved independently. Finally, we provide experimental results obtained over several deployments of a commercial inspection platform that validate the proposed techniques.This work was generously supported by the Office of Naval Research1, the MIT-Portugal Program, and the Schlumberger Technology Corporation