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

Experimental evaluation of segmentation algorithms for corner detection in sonar images

The development of AUVs represents one of the latest great achievements of engineering and science for the exploration and monitoring of the underwater world. The use of these vehicles facilitates the collection of data and monitoring of these environments, allowing us to perform previously impossible operations. Autonomous navigation continues to present many challenges. The question of total autonomy is yet to be solved. Currently, AUVs are not able to navigate without some outside assistance for long periods.When underwater, due to the high attenuation suffered by the GPS and radio-frequency signals, the use of acoustic communications and sensors offers better results.Imaging sonars have been one of the most appealing technologies for acquiring information in underwater environments because they can yield acoustic images of the surroundings and have a set of particular characteristics that are well suited for obstacle detection and characterization tasks.Corners usually appear very distinct from the rest of the scene in sonar images, generally characterized by sharp intensities in a vertical direction. The detection of corners is particularly useful in human-structured environments such as tanks because the knowledge on their position provides a way to compute the vehicle position inside it. The combination of some basic operations typically used for image segmentation can be applied to the raw sonar image to detect and localize these spots.This dissertation proposes and evaluates with experimental data a set of image segmentation algorithms for corner detection in sonar scans. A detailed description of the necessary steps to accomplish this is provided as well as a critical analysis of the results following a few relevant metrics for autonomous navigation

Numerical simulation of an automatic depth controller for an underwater vehicle

In this paper, we analyze two diff erent mathematical strategies for solving the problem which consists in controlling a depth change manoeuverability for an specifi c type of submarine. Precisely, we will apply both controllability theory and the more classical linear quadratic optimal control theory to a simplifi ed linear model obtained from the general nonlinear DTNSRDC equations of motion. Finally, numerical results will be contrasted to show the advantages and handicaps of the proposed models. It is also important to emphasize that the results presented in this work are only a fi rst step towards a better understanding of the problem

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

Underwater Localization in Complex Environments

A capacidade de um veículo autónomo submarino (AUV) se localizar num ambiente complexo, bem como de extrair características relevantes do mesmo, é de grande importância para o sucesso da navegação. No entanto, esta tarefa é particularmente desafiante em ambientes subaquáticos devido à rápida atenuação sofrida pelos sinais de sistemas de posicionamento global ou outros sinais de radiofrequência, dispersão e reflexão, sendo assim necessário o uso de processos de filtragem. Ambiente complexo é definido aqui como um cenário com objetos destacados das paredes, por exemplo, o objeto pode ter uma certa variabilidade de orientação, portanto a sua posição nem sempre é conhecida. Exemplos de cenários podem ser um porto, um tanque ou mesmo uma barragem, onde existem paredes e dentro dessas paredes um AUV pode ter a necessidade de se localizar de acordo com os outros veículos na área e se posicionar em relação ao mesmo e analisá-lo. Os veículos autónomos empregam muitos tipos diferentes de sensores para localização e percepção dos seus ambientes e dependem dos computadores de bordo para realizar tarefas de direção autónoma. Para esta dissertação há um problema concreto a resolver, localizar um cabo suspenso numa coluna de água em uma região conhecida do mar e navegar de acordo com ela. Embora a posição do cabo no mundo seja bem conhecida, a dinâmica do cabo não permite saber exatamente onde ele está. Assim, para que o veículo se localize de acordo com este para que possa ser inspecionado, a localização deve ser baseada em sensores ópticos e acústicos. Este estudo explora o processamento e a análise de imagens óticas e acústicas, por meio dos dados adquiridos através de uma câmara e por um sonar de varrimento mecânico (MSIS),respetivamente, a fim de extrair características ambientais relevantes que possibilitem a estimação da localização do veículo. Os pontos de interesse extraídos de cada um dos sensores são utilizados para alimentar um estimador de posição, implementando um Filtro de Kalman Extendido (EKF), de modo a estimar a posição do cabo e através do feedback do filtro melhorar os processos de extração de pontos de interesse utilizados.The ability of an autonomous underwater vehicle (AUV) to locate itself in a complex environment as well as to detect relevant environmental features is of crucial importance for successful navigation. However, it's particularly challenging in underwater environments due to the rapid attenuation suffered by signals from global positioning systems or other radio frequency signals, dispersion and reflection thus needing a filtering process. Complex environment is defined here as a scenario with objects detached from the walls, for example the object can have a certain orientation variability therefore its position is not always known. Examples of scenarios can be a harbour, a tank or even a dam reservoir, where there are walls and within those walls an AUV may have the need to localize itself according to the other vehicles in the area and position itself relative to one to observe, analyse or scan it. Autonomous vehicles employ many different types of sensors for localization and perceiving their environments and they depend on the on-board computers to perform autonomous driving tasks. For this dissertation there is a concrete problem to solve, which is to locate a suspended cable in a water column in a known region in the sea and navigate according to it. Although the cable position in the world is well known, the cable dynamics does not allow knowing where it is exactly. So, in order to the vehicle localize itself according to it so it can be inspected, the localization has to be based on optical and acoustic sensors. This study explores the processing and analysis of optical and acoustic images, through the data acquired through a camera and by a mechanical scanning sonar (MSIS), respectively, in order to extract relevant environmental characteristics that allow the estimation of the location of the vehicle. The points of interest extracted from each of the sensors are used to feed a position estimator, by implementing an Extended Kalman Filter (EKF), in order to estimate the position of the cable and through the feedback of the filter improve the extraction processes of points of interest used

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

RBPF-MSIS: Toward Rao-Blackwellized Particle Filter SLAM for Autonomous Underwater Vehicle With Slow Mechanical Scanning Imaging Sonar

Simultaneous localization and mapping (SLAM) has the potential to play a fundamental and significant role in achieving full autonomy for autonomous underwater vehicles (AUV). This article proposes a Rao-Blackwellized particle filter (RBPF) SLAM algorithm for an AUV equipped with a mechanically scanning imaging sonar (MSIS) that has a very slow scanning frequency. To tackle the issues of scan distortion and sonar data sparseness caused by the slow-sampling MSIS, the core of the algorithm is a carefully designed sliding window-based scan forming module. Then the formed scans are fed into the modified RBPF to build a consistent grid-based map thus localizing the AUV accurately. Extensive simulation and experiments are carried out to verify the proposed algorithm. The results show that the proposed algorithm outperforms existing ones in terms of the level of map consistency with the environment as well as the accuracy of pose estimation

Probablistic approaches for intelligent AUV localisation

This thesis studies the problem of intelligent localisation for an autonomous underwater vehicle (AUV). After an introduction about robot localisation and specific issues in the underwater domain, the thesis will focus on passive techniques for AUV localisation, highlighting experimental results and comparison among different techniques. Then, it will develop active techniques, which require intelligent decisions about the steps to undertake in order for the AUV to localise itself. The undertaken methodology consisted in three stages: theoretical analysis of the problem, tests with a simulation environment, integration in the robot architecture and field trials. The conclusions highlight applications and scenarios where the developed techniques have been successfully used or can be potentially used to enhance the results given by current techniques. The main contribution of this thesis is in the proposal of an active localisation module, which is able to determine the best set of action to be executed, in order to maximise the localisation results, in terms of time and efficiency