Underwater robots with sonar and smart tether for underground cistern mapping and exploration

This paper describes the application of using a submersible remotely operated vehicle (ROV) to map and explore underground water cisterns during a series of expeditions to Malta and Gozo. The purpose of this project was to create maps of ancient cisterns located under private homes, churches, and fortresses where passageways leading to the cisterns are too narrow and dangerous for humans to enter. These cisterns were used as water storage systems for hundreds of years, and many still contained water. The small ROV that was lowered into these cisterns was equipped with a sonar module to enable the creation of maps, two cameras to record live video, a grabber-arm for interacting with objects in the environment, and a Smart Tether to record additional positioning data of the ROV. Each of these components are discussed in terms of functionality and appropriateness for use by archaeologists wishing to explore and extract mapping information from narrow water-filled caverns. Additionally, three different mapping and localization techniques are presented including 1) Sonar image mosaics using stationary sonar scans, 2) Sonar image mosaics using stationary sonar scans with Smart Tether position data, and 3) Simultaneous Localization and Mapping (SLAM) using stationary sonar scans. Each of the algorithms used in this project have benefits in certain applications. During two expeditions in Malta and Gozo, 2-dimensional maps of 32 cisterns were successfully constructed.peer-reviewe

Real-time Monocular Visual Odometry for Turbid and Dynamic Underwater Environments

In the context of robotic underwater operations, the visual degradations induced by the medium properties make difficult the exclusive use of cameras for localization purpose. Hence, most localization methods are based on expensive navigational sensors associated with acoustic positioning. On the other hand, visual odometry and visual SLAM have been exhaustively studied for aerial or terrestrial applications, but state-of-the-art algorithms fail underwater. In this paper we tackle the problem of using a simple low-cost camera for underwater localization and propose a new monocular visual odometry method dedicated to the underwater environment. We evaluate different tracking methods and show that optical flow based tracking is more suited to underwater images than classical approaches based on descriptors. We also propose a keyframe-based visual odometry approach highly relying on nonlinear optimization. The proposed algorithm has been assessed on both simulated and real underwater datasets and outperforms state-of-the-art visual SLAM methods under many of the most challenging conditions. The main application of this work is the localization of Remotely Operated Vehicles (ROVs) used for underwater archaeological missions but the developed system can be used in any other applications as long as visual information is available

Extracting Proprioceptive Information By Analyzing Rotating Range Sensors Induced Distortion

The increased autonomy of robots is directly linked to their capability to perceive their environment. Simultaneous Localization and Mapping (SLAM) techniques, which associate perception and movement, are particularly interesting because they provide advanced autonomy to vehicles in the field of Intelligent Transportation Systems (ITS). Such ITS are based on both proprioceptive sensors to estimate their dynamics and exteroceptive sensors in order to perceive the surrounding of the vehicle. This second class of sensor is dominated by camera and rotating range sensors such as LIDAR or RADAR. Indeed, the majority of intelligent vehicles uses today 2D/3D laser or panoramic radar to localize itself or detect and avoid obstacles. The use of a rotating range sensor, while moving at high speed, creates distortions in the collected data. Such an effect is, in the majority of studies, ignored or considered as noise and then corrected, based on additional proprioceptive sensors or localization systems. In this study, rather than considering distortion as a noise, we consider that it contains all the information about the vehicle’s displacement. We propose to extract this information from such distortion without any other information than the exteroceptive sensor data. The idea is to resort to velocimetry by only analyzing the distortion of the measurements. As a result, we propose a linear and angular velocities estimator of the mobile robot based on the distortion analysis

EVALUATION OF VISION-BASED LOCALIZATION AND MAPPING TECHNIQUES IN A SUBSEA METROLOGY SCENARIO

Metrology is fundamental in all the applications that require to qualify, verify and validate measured data according to standards or, in other words, to assess their compliance with predefined tolerances. At sea, metrology is commonly associated with the process of measuring underwater structures, mainly pipeline elements widely used in offshore industry. Subsea operations are very expensive; optimizing time and money resources are the core factors driving innovation in the subsea metrology industry. In this study, the authors investigate the use of state-of-art vision-based algorithms, i.e. ORB-SLAM2 and Visual Odometry, as a navigation tool to assist and control a Remotely Operated Vehicle (ROV) while performing subsea metrology operations. In particular, the manuscript will focus on methods for assessing the accuracy of both trajectory and tie points provided by the tested approaches and evaluating whether the preliminary real time reconstruction meets the tolerances defined in typical subsea metrology scenarios

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

An Improved Otsu Threshold Segmentation Method for Underwater Simultaneous Localization and Mapping-Based Navigation

The main focus of this paper is on extracting features with SOund Navigation And Ranging (SONAR) sensing for further underwater landmark-based Simultaneous Localization and Mapping (SLAM). According to the characteristics of sonar images, in this paper, an improved Otsu threshold segmentation method (TSM) has been developed for feature detection. In combination with a contour detection algorithm, the foreground objects, although presenting different feature shapes, are separated much faster and more precisely than by other segmentation methods. Tests have been made with side-scan sonar (SSS) and forward-looking sonar (FLS) images in comparison with other four TSMs, namely the traditional Otsu method, the local TSM, the iterative TSM and the maximum entropy TSM. For all the sonar images presented in this work, the computational time of the improved Otsu TSM is much lower than that of the maximum entropy TSM, which achieves the highest segmentation precision among the four above mentioned TSMs. As a result of the segmentations, the centroids of the main extracted regions have been computed to represent point landmarks which can be used for navigation, e.g., with the help of an Augmented Extended Kalman Filter (AEKF)-based SLAM algorithm. The AEKF-SLAM approach is a recursive and iterative estimation-update process, which besides a prediction and an update stage (as in classical Extended Kalman Filter (EKF)), includes an augmentation stage. During navigation, the robot localizes the centroids of different segments of features in sonar images, which are detected by our improved Otsu TSM, as point landmarks. Using them with the AEKF achieves more accurate and robust estimations of the robot pose and the landmark positions, than with those detected by the maximum entropy TSM. Together with the landmarks identified by the proposed segmentation algorithm, the AEKF-SLAM has achieved reliable detection of cycles in the map and consistent map update on loop closure, which is shown in simulated experiments

Navigation of an underwater robotic vehicle in a structured environment based on monocular vision

Durante as últimas décadas, o desenvolvimento de veículos subaquáticos não tripulados (na sigla em inglês – UUV) permitiu a execução de atividades subaquáticas onde a presença humana não era possível. Para auxiliar a operação destes veículos e dotá-los de uma maior autonomia, existe a necessidade de determinar a sua localização no espaço 3-D. Entre as diferentes técnicas existentes para a localização subaquática, as técnicas baseadas em visão computacional são bastante atrativas pois as câmaras fazem parte do equipamento padrão de um veículo robótico subaquático, permitindo obter soluções de localizações de baixo custo. Nesta dissertação, é abordado o tema da localização subaquática com recurso a uma única câmara, a odometria visual monocular, e com recurso a uma câmara auxiliada por uma Unidade de Medição Inercial (na sigla em inglês - IMU), a odometria visual-inercial monocular. A IMU é um sensor composto por um acelerómetro, um giroscópio e um magnetómetro, os quais se encontram no veículo utilizado nesta dissertação, o Pro4 ROV da VideoRay. São explorados diferentes métodos de odometria visual-inercial e odometria visual, e a sua aplicação ao meio subaquático. Devido às condições difíceis do meio, são exploradas duas formas de melhorar a visibilidade das imagens adquiridas com o objetivo de melhorar o desempenho dos algoritmos avaliados. Como a aquisição de dados no ambiente subaquático não é trivial, não existe muita informação sobre o desempenho dos métodos utilizados no meio em estudo. Deste modo, antes de os aplicar no Pro4 ROV, juntamente com as melhorias propostas, os algoritmos foram aplicados sobre um dataset subaquático público. Devido a fatores que condicionaram a utilização do Pro4 num ambiente real e a dificuldades técnicas na leitura dos dados da IMU, foram definidos um conjunto de testes em ambiente terrestre utilizando apenas os métodos de odometria visual, com os objetivos de validar o processo de calibração efetuado e demonstrar a aplicação dos algoritmos no veículo utilizado. Os resultados obtidos com o dataset demonstram que a utilização de uma câmara e de uma IMU no meio subaquático permite obter uma solução de localização de baixo custo com uma precisão submétrica. Em particular, a visibilidade da imagem revela ser um fator determinante para o aumento dessa precisão. Relativamente ao resultados obtidos com o ROV, estes destacam a importância do aproveitamento do largo campo de visão da câmara para o desempenho da odometria visual.During the past few decades, the development of Unmanned Underwater Vehicles (UUV) has allowed underwater activities to be carried out where human presence was not possible. To assist the operation of these vehicles and provide them with greater autonomy, there is a need to determine their position in the 3-D space. Among the different existing techniques for underwater localization, techniques based on computer vision are quite attractive because cameras are part of the standard equipment of an underwater robotic vehicle, allowing to obtain low cost localization solutions. In this dissertation, the theme of underwater localization is addressed using a single camera, called monocular visual odometry, and using a camera aided by an Inertial Measurement Unit (IMU), called monocular visualinertial odometry. The IMU is a sensor composed of an accelerometer, a gyroscope and a magnetometer, which are part of the vehicle used in this dissertation, the VideoRay Pro4 ROV. Different methods of visual-inertial odometry and visual odometry are explored, including their application to the underwater environment. Due to the difficult conditions of the environment, two ways of improving the visibility of the acquired images are explored in order to improve the performance of the evaluated algorithms. As the acquisition of data in the underwater environment is not trivial, there is not much information on the performance of the methods used in the study environment. Thus, before applying them to the Pro4 ROV, as well as the proposed improvements, the algorithms were applied over a public underwater dataset. Due to factors that conditioned the use of Pro4 in a real environment and technical difficulties in reading the IMU data, a set of tests in terrestrial environment were defined using only the methods of visual odometry, with the purpose of validating the calibration process performed and demonstrate the application of the algorithms in the vehicle used. The results obtained with the dataset demonstrate that the use of a camera and an IMU in the underwater environment allows to obtain a low-cost localization solution with submetric precision. In particular, the visibility of the image proves to be a determinant factor for increasing this accuracy. Regarding the results obtained with the ROV, these highlight the importance of taking advantage of the wide field of view of the camera.Mestrado em Engenharia Eletrónica e Telecomunicaçõe

Underwater SLAM in a marina environment

Abstract — This paper describes a navigation system for autonomous underwater vehicles (AUVs) in partially structured environments, such as dams, harbors, marinas or marine platforms. A mechanical scanning imaging sonar is used to obtain information about the location of planar structures present in such environments. A modified version of the Hough transform has been developed to extract line features, together with their uncertainty, from the continuous sonar dataflow. The information obtained is incorporated into a feature-based SLAM algorithm running an Extended Kalman Filter (EKF). Simultaneously, the AUV’s position estimate is provided to the feature extraction algorithm to correct the distortions that the vehicle motion produces in the acoustic images. Experiments carried out in a marina located in the Costa Brava (Spain) with the Ictineu AUV show the viability of the proposed approach. I

Underwater SLAM in a marina environment

This paper describes a navigation system for autonomous underwater vehicles (AUVs) in partially structured environments, such as dams, harbors, marinas or marine platforms. A mechanical scanning imaging sonar is used to obtain information about the location of planar structures present in such environments. A modified version of the Hough transform has been developed to extract line features, together with their uncertainty, from the continuous sonar dataflow. The information obtained is incorporated into a feature-based SLAM algorithm running an Extended Kalman Filter (EKF). Simultaneously, the AUV's position estimate is provided to the feature extraction algorithm to correct the distortions that the vehicle motion produces in the acoustic images. Experiments carried out in a marina located in the Costa Brava (Spain) with the Ictineu AUV show the viability of the proposed approac