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

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)

Pose-based slam with probabilistic scan matching algorithm using a mechanical scanned imaging sonar

This paper proposes a pose-based algorithm to solve the full SLAM problem for an Autonomous Underwater Vehicle (AUV), navigating in an unknown and possibly unstructured environment. The technique incorporate probabilistic scan matching with range scans gathered from a Mechanical Scanned Imaging Sonar (MSIS) and the robot dead-reckoning displacements estimated from a Doppler Velocity Log (DVL) and a Motion Reference Unit (MRU). The raw data from the sensors are processed and fused in-line. No priory structural information or initial pose are considered. The algorithm has been tested on an AUV guided along a 600m path within a marina environment, showing the viability of the proposed approach.Peer Reviewe

Towards Autonomous Ship Hull Inspection using the Bluefin HAUV

URL is to paper listed on conference scheduleIn this paper we describe our effort to automate ship hull inspection for security applications. Our main contribution is a system that is capable of drift-free self-localization on a ship hull for extended periods of time. Maintaining accurate localization for the duration of a mission is important for navigation and for ensuring full coverage of the area to be inspected. We exclusively use onboard sensors including an imaging sonar to correct for drift in the vehicle’s navigation sensors. We present preliminary results from online experiments on a ship hull. We further describe ongoing work including adding capabilities for change detection by aligning vehicle trajectories of different missions based on a technique recently developed in our lab.United States. Office of Naval Research (grant N00014-06- 10043

Advanced perception, navigation and planning for autonomous in-water ship hull inspection

Inspection of ship hulls and marine structures using autonomous underwater vehicles has emerged as a unique and challenging application of robotics. The problem poses rich questions in physical design and operation, perception and navigation, and planning, driven by difficulties arising from the acoustic environment, poor water quality and the highly complex structures to be inspected. In this paper, we develop and apply algorithms for the central navigation and planning problems on ship hulls. These divide into two classes, suitable for the open, forward parts of a typical monohull, and for the complex areas around the shafting, propellers and rudders. On the open hull, we have integrated acoustic and visual mapping processes to achieve closed-loop control relative to features such as weld-lines and biofouling. In the complex area, we implemented new large-scale planning routines so as to achieve full imaging coverage of all the structures, at a high resolution. We demonstrate our approaches in recent operations on naval ships.United States. Office of Naval Research (Grant N00014-06-10043)United States. Office of Naval Research (Grant N00014-07-1-0791

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

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

Improving Generalization of Synthetically Trained Sonar Image Descriptors for Underwater Place Recognition

Autonomous navigation in underwater environments presents challenges due to factors such as light absorption and water turbidity, limiting the effectiveness of optical sensors. Sonar systems are commonly used for perception in underwater operations as they are unaffected by these limitations. Traditional computer vision algorithms are less effective when applied to sonar-generated acoustic images, while convolutional neural networks (CNNs) typically require large amounts of labeled training data that are often unavailable or difficult to acquire. To this end, we propose a novel compact deep sonar descriptor pipeline that can generalize to real scenarios while being trained exclusively on synthetic data. Our architecture is based on a ResNet18 back-end and a properly parameterized random Gaussian projection layer, whereas input sonar data is enhanced with standard ad-hoc normalization/prefiltering techniques. A customized synthetic data generation procedure is also presented. The proposed method has been evaluated extensively using both synthetic and publicly available real data, demonstrating its effectiveness compared to state-of-the-art methods.Comment: This paper has been accepted for publication at the 14th International Conference on Computer Vision Systems (ICVS 2023