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

Toward autonomous exploration in confined underwater environments

Author Posting. © The Author(s), 2015. This is the author's version of the work. It is posted here by permission of John Wiley & Sons for personal use, not for redistribution. The definitive version was published in Journal of Field Robotics 33 (2016): 994-1012, doi:10.1002/rob.21640.In this field note we detail the operations and discuss the results of an experiment conducted in the unstructured environment of an underwater cave complex, using an autonomous underwater vehicle (AUV). For this experiment the AUV was equipped with two acoustic sonar to simultaneously map the caves’ horizontal and vertical surfaces. Although the caves’ spatial complexity required AUV guidance by a diver, this field deployment successfully demonstrates a scan matching algorithm in a simultaneous localization and mapping (SLAM) framework that significantly reduces and bounds the localization error for fully autonomous navigation. These methods are generalizable for AUV exploration in confined underwater environments where surfacing or pre-deployment of localization equipment are not feasible and may provide a useful step toward AUV utilization as a response tool in confined underwater disaster areas.This research work was partially sponsored by the EU FP7-Projects: Tecniospring- Marie Curie (TECSPR13-1-0052), MORPH (FP7-ICT-2011-7-288704), Eurofleets2 (FP7-INF-2012-312762), and the National Science Foundation (OCE-0955674)

Autonomous Underwater Vehicles as Tools for Deep-Submergence Archaeology

Marine archaeology beyond the capabilities of scuba divers is a technologically enabled field. The tool suite includes ship-based systems such as towed side-scan sonars and remotely operated vehicles, and more recently free-swimming autonomous underwater vehicles (AUVs). Each of these platforms has various imaging and mapping capabilities appropriate for specific scales and tasks. Broadly speaking, AUVs are becoming effective tools for locating, identifying, and surveying archaeological sites. This paper discusses the role of AUVs in this suite of tools, outlines some specific design criteria necessary to maximize their utility in the field, and presents directions for future developments. Results are presented for a recent joint AUV–towed system survey and a demonstration of current mine-hunting technologies applied to archaeology

Localization, Mapping and SLAM in Marine and Underwater Environments

The use of robots in marine and underwater applications is growing rapidly. These applications share the common requirement of modeling the environment and estimating the robots’ pose. Although there are several mapping, SLAM, target detection and localization methods, marine and underwater environments have several challenging characteristics, such as poor visibility, water currents, communication issues, sonar inaccuracies or unstructured environments, that have to be considered. The purpose of this Special Issue is to present the current research trends in the topics of underwater localization, mapping, SLAM, and target detection and localization. To this end, we have collected seven articles from leading researchers in the field, and present the different approaches and methods currently being investigated to improve the performance of underwater robots

Design of the obstacle detection system with the SONAR MK3 on Guanay II AUV

Autonomous underwater vehicles (AUV) perform inspection missions and intervention in known and unknown environments, where it is necessary to ensure their safety. The AUV must have the ability to detect and avoid obstacles in the path of navigation. This article, an obstacle detection system for experimental Guanay II AUV is proposed, using a mechanical scanning SONAR Tritech Micron MK3. Since Guanay II operates autonomously, we have designed software that allows adjustment and control of the parameters of SONAR, and the acquisition and processing of the signals obtained. Experimental tests at sea have allowed to verify the correct operation of software designed, as well as, experimental tests in a controlled environment have allowed to determine the optimal values of the basic parameters of SONAR.Postprint (published version

CES-515 Towards Localization and Mapping of Autonomous Underwater Vehicles: A Survey

Autonomous Underwater Vehicles (AUVs) have been used for a huge number of tasks ranging from commercial, military and research areas etc, while the fundamental function of a successful AUV is its localization and mapping ability. This report aims to review the relevant elements of localization and mapping for AUVs. First, a brief introduction of the concept and the historical development of AUVs is given; then a relatively detailed description of the sensor system used for AUV navigation is provided. As the main part of the report, a comprehensive investigation of the simultaneous localization and mapping (SLAM) for AUVs are conducted, including its application examples. Finally a brief conclusion is summarized

Active SLAM for autonomous underwater exploration

Exploration of a complex underwater environment without an a priori map is beyond the state of the art for autonomous underwater vehicles (AUVs). Despite several efforts regarding simultaneous localization and mapping (SLAM) and view planning, there is no exploration framework, tailored to underwater vehicles, that faces exploration combining mapping, active localization, and view planning in a unified way. We propose an exploration framework, based on an active SLAM strategy, that combines three main elements: a view planner, an iterative closest point algorithm (ICP)-based pose-graph SLAM algorithm, and an action selection mechanism that makes use of the joint map and state entropy reduction. To demonstrate the benefits of the active SLAM strategy, several tests were conducted with the Girona 500 AUV, both in simulation and in the real world. The article shows how the proposed framework makes it possible to plan exploratory trajectories that keep the vehicle’s uncertainty bounded; thus, creating more consistent maps.Peer ReviewedPostprint (published version

An Overview of AUV Algorithms Research and Testbed at the University of Michigan

This paper provides a general overview of the autonomous underwater vehicle (AUV) research projects being pursued within the Perceptual Robotics Laboratory (PeRL) at the University of Michigan. Founded in 2007, PeRL's research thrust is centered around improving AUV autonomy via algorithmic advancements in sensor-driven perceptual feedback for environmentally-based real-time mapping, navigation, and control. In this paper we discuss our three major research areas of: (1) real-time visual simultaneous localization and mapping (SLAM); (2) cooperative multi-vehicle navigation; and (3) perception-driven control. Pursuant to these research objectives, PeRL has acquired and significantly modified two commercial off-the-shelf (COTS) Ocean-Server Technology, Inc. Iver2 AUV platforms to serve as a real-world engineering testbed for algorithm development and validation. Details of the design modification, and related research enabled by this integration effort, are discussed herein.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/86058/1/reustice-15.pd

Underwater localization using imaging sonars in 3D environments

This work proposes a localization method using a mechanically scanned imaging sonar (MSIS), which stands out by its low cost and weight. The proposed method implements a Particle Filter, a Bayesian Estimator, and introduces a measurement model based on sonar simulation theory. To the best of author’s knowledge, there is no similar approach in the literature, as sonar simulation current methods target in syntethic data generation, mostly for object recognition . This stands as the major contribution of the thesis as allows the introduction of the computation of intensity values provided by imaging sonars, while maitaining compability with the already used methods, such as range extraction. Simulations shows the efficiency of the method as well its viability to the utilization of imaging sonar in underwater localization. The new approach make possible, under certain constraints, the extraction of 3D information from a sensor considered, in the literature, as 2D and also in situations where there is no reference at the same horizontal plane of the sensor transducer scanning axis. The localization in complex 3D environment is also an advantage provided by the proposed method.Este trabalho propõe um método de localização utilizando um sonar do tipo MSIS (Mechanically Scanned Imaging Sonar ), o qual se destaca por seu baixo custo e peso. O método implementa um filtro de partículas, um estimador Bayesiano, e introduz um modelo de medição baseado na teoria de simulação de sonares. No conhecimento do autor não há uma abordagem similar na literatura, uma vez que os métodos atuais de simulação de sonar visam a geração de dados sintéticos para o reconhecimento de objetos. Esta é a maior contribuição da tese pois permite a a computação dos valores de intensidade fornecidos pelos sonares do tipo imaging e ao mesmo tempo é compatível com os métodos já utilizados, como extração de distância. Simulações mostram o bom desempenho do método, assim como sua viabilidade para o uso de imaging sonars na localização submarina. A nova abordagem tornou possível, sob certas restrições, a extração de informações 3D de um sensor considerado, na literatura, como somente 2D e também em situações em que não há nehnuma referência no mesmo plano horizontal do eixo de escaneamento do transdutor. A localização em ambientes 3D complexos é também uma vantagem proporcionada pelo método proposto

Towards a robust slam framework for resilient AUV navigation

Autonomous Underwater Vehicles (AUVs) are playing an increasing part in modern navies, to the point that the control of oceans will soon be decided by their strategic use. In face of more complex missions occurring in potentially hostile environments, the resilience of such systems becomes critical. In this study, we investigate the following scenario: how does a lone AUV could recover from a temporary breakdown that has created a gap in its measurements, while remaining beneath the surface to avoid detection? It is assumed that the AUV is equipped with an active sonar and is operating in an uncharted area. The vehicle has to rely on itself by recovering its location using a Simultaneous Localization and Mapping (SLAM) algorithm. While SLAM is widely investigated and developed in the case of aerial and terrestrial robotics, the nature of the poorly structured underwater environment dramatically challenges its effectiveness. To address such a complex problem, the usual side scan sonar data association techniques are investigated under a global registration problem while applying robust graph SLAM modelling. In particular, ways to improve the global detection of features from sonar mosaic region patches that react well to the MICR similarity measure are discussed. The main contribution of this study is centered on a novel data processing framework that is able to generate different graph topologies using robust SLAM techniques. One of its advantages is to facilitate the testing of different modelling hypotheses to tackle the data gap following the temporary breakdown and make the most of the limited available information. Several research perspectives related to this framework are discussed. Notably, the possibility to further extend the proposed framework to heterogeneous datasets and the opportunity to accelerate the recovery process by inferring information about the breakdown using machine learning.PH