C3 - Artigos Publicados em Periódicos
URI permanente para esta coleçãohttps://rihomolog.furg.br/handle/1/486
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12 resultados
Resultados da Pesquisa
- ItemA visual system for distributed mosaics using an auvs fleet(2009) Botelho, Silvia Silva da Costa; Drews Junior, Paulo Lilles Jorge; Galeriano, Marcelo de Paiva; Gonçalves, Eder Mateus NunesThe use of teams of Autonomous Underwater Vehicles for visual inspection tasks is a promising robotic field. The images captured by different robots can be also to aid in the localization/navigation of the fleet. In a previous work, a distributed localization system was presented based on the use of Augmented States Kalman Filter through the visual maps obtained by the fleet. In this context, this paper details a system for on-line construction of visual maps and its use to aid the localization and navigation of the robots. Different aspects related to the capture, treatment and construction of mosaics by fleets of robots are presented. The developed system can be executed on-line on different robotic platforms. The paper is concluded with a series of tests and analyses aiming at to system validation.
- ItemA visual system for distributed mosaics using AUV fleets(2009) Drews Junior, Paulo Lilles Jorge; Botelho, Silvia Silva da CostaThe article explores the self-localization and mapping for fleets of multi-autonomous underwater vehicles using visual-based systems. It evaluates the generated mosaics captured by cameras of vehicles. It discusses the mosaic construction concept. It demonstrates that these mosaics can be used as reference maps for the vehicles navigation system.
- ItemMicroalgae classification using semi-supervised and active learning based on Gaussian mixture models(2013) Drews Junior, Paulo Lilles Jorge; Colares, Rafael Gonçalves; Machado, Pablo; Faria, Matheus de; Detoni, Amália Maria Sacilotto; Tavano, Virginia MariaMicroalgae are unicellular organisms that have different shapes, sizes and structures. Classifying these microalgae manually can be an expensive task, because thousands of microalgae can be found in even a small sample of water. This paper presents an approach for an automatic/semi-automatic classification ofmicroalgae based on semi-supervised and active learning algorithms, using Gaussian mixturemodels. The results showthat the approach has an excellent cost-benefit relation, classifying more than 90 % of microalgae in a well distributed way, overcoming the supervised algorithm SVM.
- ItemSpatial density patterns for efficient change detection in 3D environment for autonomous surveillance robots(2014) Vieira, Antônio Wilson; Drews Junior, Paulo Lilles Jorge; Campos, Mario Fernando MontenegroThe ability to detect changes is an essential competence that robots should possess for increased autonomy. In several applications, such as surveillance, a robot needs to detect relevant changes in the environment by comparing current sensory data with previously acquired information from the environment. We present an efficient method for point cloud comparison and change detection in 3D environments based on spatial density patterns. Our method automatically segments 3D data corrupted by noise and outliers into an implicit volume bounded by a surface, making it possible to efficiently apply Boolean operations in order to detect changes and to update existing maps. The method has been validated on several trials using mobile robots operating in real environments and its performance was compared to state-of-the-art algorithms. Our results demonstrate the performance of the proposed method, both in greater accuracy and reduced computational cost.
- ItemAutomatic control of a ROV for inspection of underwater structures using a low-cost sensing(2015) Kuhn, Vinicius Nizolli; Drews Junior, Paulo Lilles Jorge; Gomes, Sebastião Cícero Pinheiro; Cunha, Mauro André Barbosa; Botelho, Silvia Silva da CostaThis work deals with the implementation of a position and orientation automatic control of an underwater vehicle to perform inspection tasks of submerged structures without using the knowledge of a previous dynamic model in the control law and, mainly, by using a low-cost embedded minimal instrumentation. This instrumentation does not employ expensive components to determine the position and orientation of the vehicle, like a central inertial. In this way, a computer vision system is used as a sensory source in order to assist the control. It was developed an algorithm to image processing and a system for integrating the different sensors. Experimental results using the proposed sensing show that the closed-loop control of the vehicle was suitable for the conduction of inspections.
- ItemControlling a system for underwater visual inspection(2013) Drews Junior, Paulo Lilles Jorge; Kuhn, Vinicius Nizolli; Gomes, Sebastião Cícero PinheiroNowadays, the ocean plays a fundamental role in the global economy, mainly due to oil extraction industry. It makes the environment be populated with human-made structures that needs to be inspected and maintained. In this context, this paper details a system for online detect an underwater cable-like target using computer vision algorithms, as well as an automatic control of a vehicle to tracking it. This system could be used to assist a human operator during visual inspection tasks. This work is concluded with a series of tests and analyses aiming to the system validation.
- ItemA novel robust scene change detection algorithm for autonomous robots using mixtures of gaussians(2014) Manso, Luis; Núñez, Pedro; Silva, Sidnei da; Drews Junior, Paulo Lilles JorgeInterest in change detection techniques has considerably increased during recent years in the field of autonomous robotics. This is partly because changes in a robot’s working environment are useful for several robotic skills (e.g., spatial cognition, modelling or navigation) and applications (e.g., surveillance or guidance robots). Changes are usually detected by comparing current data provided by the robot’s sensors with a previously known map or model of the environment. When the data consists of a large point cloud, dealing with it is a computationally expensive task, mainly due to the amount of points and the redundancy. Using Gaussian Mixture Models (GMM) instead of raw point clouds leads to a more compact feature space that can be used to efficiently process the input data. This allows us to successfully segment the set of 3D points acquired by the sensor and reduce the computational load of the change detection algorithm. However, the segmentation of the environment as a Mixture of Gaussians has some problems that need to be properly addressed. In this paper, a novel change detection algorithm is described in order to improve the robustness and computational cost of previous approaches. The proposal is based on the classic Expectation Maximization (EM) algorithm, for which different selection criteria are evaluated. As demonstrated in the experimental results section, the proposed change detection algorithm achieves the detection of changes in the robot’s working environment faster and more accurately than similar approaches.
- ItemAppearance-based odometry and mapping with feature descriptors for underwater robots(2009) Botelho, Silvia Silva da Costa; Drews Junior, Paulo Lilles Jorge; Figueiredo, Mônica da Silva; Rocha, Celina Haffele da; Oliveira, Gabriel LeivasThe use of Autonomous Underwater Vehicles (AUVs) for underwater tasks is a promising robotic field. These robots can carry visual inspection cameras. Besides serving the activities of inspection and mapping, the captured images can also be used to aid navigation and localization of the robots. Visual odometry is the process of determining the position and orientation of a robot by analyzing the associated camera images. It has been used in a wide variety of non-standard locomotion robotic methods. In this context, this paper proposes an approach to visual odometry and mapping of underwater vehicles. Supposing the use of inspection cameras, this proposal is composed of two stages: i) the use of computer vision for visual odometry, extracting landmarks in underwater image sequences and ii) the development of topological maps for localization and navigation. The integration of such systems will allow visual odometry, localization and mapping of the environment. A set of tests with real robots was accomplished, regarding online and performance issues. The results reveals an accuracy and robust approach to several underwater conditions, as illumination and noise, leading to a promissory and original visual odometry and mapping technique.
- ItemMapas auto-organizáveis em veículos autônomos subaquáticos(2008) Botelho, Silvia Silva da Costa; Rocha, Celina Häffele da; Figueiredo, Mônica da Silva; Oliveira, Gabriel Leivas; Drews Junior, Paulo Lilles JorgeO uso de veículos autônomos subaquáticos (AUVs) para tarefas submarinas é um campo promissor da robótica. Estes robôs podem transportar uma câmera de inspeção visual, que além de inspecionar e mapear, as imagens capturadas podem auxiliar a navegação e localização dos robôs. Neste contexto, este trabalho propõe uma abordagem para o mapeamento destes veículos. Supondo o uso de câmeras de inspeção, esta proposta é composta pelo desenvolvimento de mapas topológicos utilizando mapas autoorganizáveis e estruturas celulares crescente (GCS) para a localização e navegação. Uma série de testes foram realizados, em relação a problemas de desempenho online. Os resultados revelaram uma boa precisão e robustez para uma série de condições subaquáticas, como iluminação e ruído, mostrando ser uma técnica de mapeamento visual promissora e original.
- ItemNovelty detection and segmentation based on gaussian mixture models: a case study in 3D robotic laser mapping(2013) Drews Junior, Paulo Lilles Jorge; Núñez, Pedro; Rocha, Rui Paulo Pinto da; Campos, Mario Fernando Montenegro; Dias, JorgeThis article proposes a framework to detect and segment changes in robotics datasets, using 3D robotic mapping as a case study. The problem is very relevant in several application domains, not necessarily related with mobile robotics, including security, health, industry and military applications. The aim is to identify significant changes by comparing current data with previous data provided by sensors. This feature is extremely challenging because large amounts of noisy data must be processed in a feasible way. The proposed framework deals with novelty detection and segmentation in robotic maps using clusters provided by Gaussian Mixture Models (GMMs). GMMs provides a feature space that enables data compression and effective processing. Two alternative criteria to detect changes in the GMM space are compared: a greedy technique based on the Earth Mover’s Distance (EMD); and a structural matching algorithm that fulfills both absolute (global matching) and relative constraints (structural matching). The proposed framework is evaluated with real robotic datasets and compared with other methods known from literature. With this purpose, 3D mapping experiments are carried out with both simulated data and real data from a mobile robot equipped with a 3D range sensor.