Cookies
Usamos cookies para melhorar nosso site e a sua experiência. Ao continuar a navegar no site, você aceita a nossa política de cookies. Ver mais
Aceitar Rejeitar
  • Menu
Sobre
Download foto HD

Sobre

Aníbal Matos concluiu o doutoramento em Engenharia Electrotécnica e de Computadores pela Universidade do Porto em 2001. É atualmente coordenador do Centro de Robótica e Sistemas Autónomos do INESC TEC e professor auxiliar na Faculdade de Engenharia da Universidade do Porto. Os seus interesses principais de investigação são perceção, navegação e controlo de veículos robóticos aquáticos, sendo autor ou coautor de mais de 80 publicações em revistas e conferências internacionais. Tem participado e liderado projetos de investigação em robótica aquática e nas suas aplicações em monitorização, inspeção, busca e salvamento e defesa.

Tópicos
de interesse
Detalhes

Detalhes

026
Publicações

2020

Deep Learning for Underwater Visual Odometry Estimation

Autores
Teixeira, B; Silva, H; Matos, A; Silva, E;

Publicação
IEEE Access

Abstract

2020

MARESye: A hybrid imaging system for underwater robotic applications

Autores
Pinto, AM; Matos, AC;

Publicação
INFORMATION FUSION

Abstract
This article presents an innovative hybrid imaging system that provides dense and accurate 3D information from harsh underwater environments. The proposed system is called MARESye and captures the advantages of both active and passive imaging methods: multiple light stripe range (LSR) and a photometric stereo (PS) technique, respectively. This hybrid approach fuses information from these techniques through a data-driven formulation to extend the measurement range and to produce high density 3D estimations in dynamic underwater environments. This hybrid system is driven by a gating timing approach to reduce the impact of several photometric issues related to the underwater environments such as, diffuse reflection, water turbidity and non-uniform illumination. Moreover, MARESye synchronizes and matches the acquisition of images with sub-sea phenomena which leads to clear pictures (with a high signal-to-noise ratio). Results conducted in realistic environments showed that MARESye is able to provide reliable, high density and accurate 3D data. Moreover, the experiments demonstrated that the performance of MARESye is less affected by sub-sea conditions since the SSIM index was 0.655 in high turbidity waters. Conventional imaging techniques obtained 0.328 in similar testing conditions. Therefore, the proposed system represents a valuable contribution for the inspection of maritime structures as well as for the navigation procedures of autonomous underwater vehicles during close range operations.

2020

MViDO: A high performance monocular vision-based system for docking a hovering AUV

Autores
Figueiredo, AB; Matos, AC;

Publicação
Applied Sciences (Switzerland)

Abstract
This paper presents a high performance (low computationally demanding) monocular vision-based system for a hovering Autonomous Underwater Vehicle (AUV) in the context of autonomous docking process-MViDO system: Monocular Vision-based Docking Operation aid. The MViDO consists of three sub-modules: a pose estimator, a tracker and a guidance sub-module. The system is based on a single camera and a three spherical color markers target that signal the docking station. The MViDO system allows the pose estimation of the three color markers even in situations of temporary occlusions, being also a system that rejects outliers and false detections. This paper also describes the design and implementation of the MViDO guidance module for the docking manoeuvres. We address the problem of driving the AUV to a docking station with the help of the visual markers detected by the on-board camera, and show that by adequately choosing the references for the linear degrees of freedom of the AUV, the AUV is conducted to the dock while keeping those markers in the field of view of the on-board camera. The main concepts behind the MViDO are provided and a complete characterization of the developed system is presented from the formal and experimental point of view. To test and evaluate the MViDO detector and pose an estimator module, we created a ground truth setup. To test and evaluate the tracker module we used the MARES AUV and the designed target in a four-meter tank. The performance of the proposed guidance law was tested on simulink/Matlab. © 2020 by the authors.

2019

Tracking multiple Autonomous Underwater Vehicles

Autores
Melo, J; Matos, AC;

Publicação
Autonomous Robots

Abstract
In this paper we present a novel method for the acoustic tracking of multiple Autonomous Underwater Vehicles. While the problem of tracking a single moving vehicle has been addressed in the literature, tracking multiple vehicles is a problem that has been overlooked, mostly due to the inherent difficulties on data association with traditional acoustic localization networks. The proposed approach is based on a Probability Hypothesis Density Filter, thus overcoming the data association problem. Our tracker is able not only to successfully estimate the positions of the vehicles, but also their velocities. Moreover, the tracker estimates are labelled, thus providing a way to establish track continuity of the targets. Using real word data, our method is experimentally validated and the performance of the tracker is evaluated. © 2018 Springer Science+Business Media, LLC, part of Springer Nature

2019

A data-driven particle filter for terrain based navigation of sensor-limited autonomous underwater vehicles

Autores
Melo, J; Matos, A;

Publicação
Asian Journal of Control

Abstract

Teses
supervisionadas

2019

Monocular vision-based system for docking a hovering AUV

Autor
André Bianchi de Aguiar Araújo de Figueiredo

Instituição
UP-FEUP

2019

Robot Localization and Mapping in Dynamic Underwater Environments

Autor
António João Almeida Bernardo Ferreira

Instituição
UP-FEUP

2019

Distributed Perception from Multiple Intelligent Systems for Offshore Marine Surveys

Autor
Daniel Filipe Barros Campos

Instituição
UP-FEUP

2019

Active Cooperative Perception for UAVs Teams

Autor
Guilherme Marques Amaral Silva

Instituição
UP-FEUP

2019

Deep Learning Approaches Assessment for Underwater Scene Understanding and Egomotion Estimation

Autor
Bernardo Gomes Teixeira

Instituição
UP-FEUP