1,120 research outputs found
Object classification in semi structured enviroment using forward-looking sonar
La exploración submarina utilizando robots ha ido en aumento en los últimos años. La automatización de tareas tales como monitoreo, inspección y mantenimiento bajo el agua requiere la comprensión del entorno del robot. El reconocimiento de objetos en la escena se está convirtiendo en un problema crítico para estos sistemas. En este trabajo, se estudia una tubería de clasificación de objetos bajo el agua aplicada en imágenes acústicas adquiridas por Forward-Looking Sonar (FLS). La segmentación de objetos combina el umbral, la búsqueda de píxeles conectados y las técnicas de análisis de picos de intensidad. El descriptor del objeto extrae la intensidad y las características geométricas de los objetos detectados. Se presenta una comparación entre los clasificadores Máquina de vectores de soporte, Vecinos más cercanos a K y Árboles aleatorios. Se desarrolló una herramienta de código abierto para anotar y clasificar los objetos y evaluar su rendimiento de clasificación. El método propuesto segmenta y clasifica eficientemente las estructuras en la escena utilizando un conjunto de datos real adquirido por un vehículo submarino en un área de puerto. Los resultados experimentales demuestran la solidez y precisión del método descrito en este documento.The submarine exploration using robots has been increasing in recent years. The automation of tasks such as monitoring, inspection, and underwater maintenance requires the understanding of the robot’s environment. The object recognition in the scene is becoming a critical issue for these systems. On this work, an underwater object classification pipeline applied in acoustic images acquired by Forward-Looking Sonar (FLS) are studied. The object segmentation combines thresholding, connected pixels searching and peak of intensity analyzing techniques. The object descriptor extract intensity and geometric features of the detected objects. A comparison between the Support Vector Machine, K-Nearest Neighbors, and Random Trees classifiers are presented. An open-source tool was developed to annotate and classify the objects and evaluate their classification performance. The proposed method efficiently segments and classifies the structures in the scene using a real dataset acquired by an underwater vehicle in a harbor area. Experimental results demonstrate the robustness and accuracy of the method described in this paper.• National Institute of Science and Technology - Integrated Oceanography and Multiple Uses of the Continental Shelf and Adjacent Ocean - Integrated Oceanography Center INCT-Mar COI funded by CNPq. Beca 610012/2011-8
• BS-NAVLOC (CAPES no 321/15, DGPU 7523 / 14-9, proyecto MEC PHBP14 / 00083)peerReviewe
OBJECT PERCEPTION IN UNDERWATER ENVIRONMENTS: A SURVEY ON SENSORS AND SENSING METHODOLOGIES
Underwater robots play a critical role in the marine industry. Object perception is the foundation for the automatic
operations of submerged vehicles in dynamic aquatic environments. However, underwater perception
encounters multiple environmental challenges, including rapid light attenuation, light refraction, or backscattering
effect. These problems reduce the sensing devices’ signal-to-noise ratio (SNR), making underwater
perception a complicated research topic. This paper describes the state-of-the-art sensing technologies and
object perception techniques for underwater robots in different environmental conditions. Due to the current
sensing modalities’ various constraints and characteristics, we divide the perception ranges into close-range,
medium-range, and long-range. We survey and describe recent advances for each perception range and suggest
some potential future research directions worthy of investigating in this field
The JASON remotely operated vehicle system
The JASON remotely operated vehicle (ROV) system has been under development for the last decade. After a number of
engineering test cruises, including the discovery of the R.M.S. Titanic and the German Battleship Bismarck, this ROV system is
now being implemented in oceanographic investigations. This paper explains its development history and its unique ability to
carry out a broad range of scientific research.Funding was provided by the Office of Naval Research
under Contract No. NOOOI4-90-J-1912
Self-supervised Learning for Sonar Image Classification
Self-supervised learning has proved to be a powerful approach to learn image
representations without the need of large labeled datasets. For underwater
robotics, it is of great interest to design computer vision algorithms to
improve perception capabilities such as sonar image classification. Due to the
confidential nature of sonar imaging and the difficulty to interpret sonar
images, it is challenging to create public large labeled sonar datasets to
train supervised learning algorithms. In this work, we investigate the
potential of three self-supervised learning methods (RotNet, Denoising
Autoencoders, and Jigsaw) to learn high-quality sonar image representation
without the need of human labels. We present pre-training and transfer learning
results on real-life sonar image datasets. Our results indicate that
self-supervised pre-training yields classification performance comparable to
supervised pre-training in a few-shot transfer learning setup across all three
methods. Code and self-supervised pre-trained models are be available at
https://github.com/agrija9/ssl-sonar-imagesComment: 8 pages, 10 figures, with supplementary. LatinX in CV Workshop @ CVPR
2022 Camera Read
Internet of Underwater Things and Big Marine Data Analytics -- A Comprehensive Survey
The Internet of Underwater Things (IoUT) is an emerging communication
ecosystem developed for connecting underwater objects in maritime and
underwater environments. The IoUT technology is intricately linked with
intelligent boats and ships, smart shores and oceans, automatic marine
transportations, positioning and navigation, underwater exploration, disaster
prediction and prevention, as well as with intelligent monitoring and security.
The IoUT has an influence at various scales ranging from a small scientific
observatory, to a midsized harbor, and to covering global oceanic trade. The
network architecture of IoUT is intrinsically heterogeneous and should be
sufficiently resilient to operate in harsh environments. This creates major
challenges in terms of underwater communications, whilst relying on limited
energy resources. Additionally, the volume, velocity, and variety of data
produced by sensors, hydrophones, and cameras in IoUT is enormous, giving rise
to the concept of Big Marine Data (BMD), which has its own processing
challenges. Hence, conventional data processing techniques will falter, and
bespoke Machine Learning (ML) solutions have to be employed for automatically
learning the specific BMD behavior and features facilitating knowledge
extraction and decision support. The motivation of this paper is to
comprehensively survey the IoUT, BMD, and their synthesis. It also aims for
exploring the nexus of BMD with ML. We set out from underwater data collection
and then discuss the family of IoUT data communication techniques with an
emphasis on the state-of-the-art research challenges. We then review the suite
of ML solutions suitable for BMD handling and analytics. We treat the subject
deductively from an educational perspective, critically appraising the material
surveyed.Comment: 54 pages, 11 figures, 19 tables, IEEE Communications Surveys &
Tutorials, peer-reviewed academic journa
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
A COLLISION AVOIDANCE SYSTEM FOR AUTONOMOUS UNDERWATER VEHICLES
The work in this thesis is concerned with the development of a novel and practical collision
avoidance system for autonomous underwater vehicles (AUVs). Synergistically,
advanced stochastic motion planning methods, dynamics quantisation approaches,
multivariable tracking controller designs, sonar data processing and workspace representation,
are combined to enhance significantly the survivability of modern AUVs.
The recent proliferation of autonomous AUV deployments for various missions such
as seafloor surveying, scientific data gathering and mine hunting has demanded a substantial
increase in vehicle autonomy. One matching requirement of such missions is
to allow all the AUV to navigate safely in a dynamic and unstructured environment.
Therefore, it is vital that a robust and effective collision avoidance system should be
forthcoming in order to preserve the structural integrity of the vehicle whilst simultaneously
increasing its autonomy.
This thesis not only provides a holistic framework but also an arsenal of computational
techniques in the design of a collision avoidance system for AUVs. The
design of an obstacle avoidance system is first addressed. The core paradigm is the
application of the Rapidly-exploring Random Tree (RRT) algorithm and the newly
developed version for use as a motion planning tool. Later, this technique is merged
with the Manoeuvre Automaton (MA) representation to address the inherent disadvantages
of the RRT. A novel multi-node version which can also address time varying
final state is suggested. Clearly, the reference trajectory generated by the aforementioned
embedded planner must be tracked. Hence, the feasibility of employing the
linear quadratic regulator (LQG) and the nonlinear kinematic based state-dependent
Ricatti equation (SDRE) controller as trajectory trackers are explored.
The obstacle detection module, which comprises of sonar processing and workspace
representation submodules, is developed and tested on actual sonar data acquired
in a sea-trial via a prototype forward looking sonar (AT500). The sonar processing
techniques applied are fundamentally derived from the image processing perspective.
Likewise, a novel occupancy grid using nonlinear function is proposed for the
workspace representation of the AUV. Results are presented that demonstrate the
ability of an AUV to navigate a complex environment.
To the author's knowledge, it is the first time the above newly developed methodologies
have been applied to an A UV collision avoidance system, and, therefore, it is
considered that the work constitutes a contribution of knowledge in this area of work.J&S MARINE LT
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