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Sobre

Aníbal Matos concluiu o doutoramento em Engenharia Electrotécnica e de Computadores pela Universidade do Porto em 2001. É atualmente professor associado na Faculdade de Engenharia da Universidade do Porto e membro do Conselho de Administração do INESC TEC. 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

030
Publicações

2021

Multi-domain inspection of offshore wind farms using an autonomous surface vehicle

Autores
Campos, DF; Matos, A; Pinto, AM;

Publicação
SN Applied Sciences

Abstract
AbstractThe offshore wind power industry is an emerging and exponentially growing sector, which calls to a necessity for a cyclical monitoring and inspection to ensure the safety and efficiency of the wind farm facilities. Thus, the emersed (aerial) and immersed (underwater) scenarios must be reconstructed to create a more complete and reliable map that maximizes the observability of all the offshore structures from the wind turbines to the cable arrays, presenting a multi domain scenario.This work proposes the use of an Autonomous Surface Vehicle (ASV) to map both domains simultaneously. As such, it will produce a multi-domain map through the fusion of navigational sensors, GPS and IMU, to localize the vehicle and aid the registration process for the perception sensors, 3D Lidar and Multibeam echosounder sonar. The performed experiments demonstrate the ability of the multi-domain mapping architecture to provide an accurate reconstruction of both scenarios into a single representation using the odometry system as the initial seed to further improve the map with data filtering and registration processes. An error of 0.049 m for the odometry estimation is observed with the GPS/IMU fusion for simulated data and 0.07 m for real field tests. The multi-domain map methodology requires an average of 300 ms per iteration to reconstruct the environment, with an error of at most 0.042 m in simulation.

2021

Project and Control Allocation of a 3 DoF Autonomous Surface Vessel With Aerial Azimuth Propulsion System

Autores
da Silva, MF; Honorio, LMD; dos Santos, MF; Neto, AFD; Cruz, NA; Matos, ACC; Westin, LGF;

Publicação
IEEE ACCESS

Abstract
To gather hydrological measurements is a difficult task for Autonomous Surface Vessels. It is necessary for precise navigation considering underwater obstacles, shallow and fast water flows, and also mitigate misreadings due to disturbs caused by their propulsion system. To deal with those problems, this paper presents a new topology of an Autonomous Surface Vessel (ASV) based on a catamaran boat with an aerial propulsion system with azimuth control. This set generates an over-actuated 3 Degree of Freedom (DoF) ASV, highly maneuverable and able of operating over the above-mentioned situations. To deal with the high computational cost of the over-actuated control allocation (CA) problem, this paper also proposes a Fast CA (FCA) approach. The FCA breaks the initial nonlinear system into partially-dependent linear subsystems. This approach generates smaller connected systems with overlapping solution spaces, generating fast and robust convergence, especially attractive for embedded control devices. Both proposals, i.e., ASV and FCA, are assessed through mathematical simulations and real scenarios.

2021

Evaluation of Bags of Binary Words for Place Recognition in Challenging Scenarios

Autores
Gaspar, AR; Nunes, A; Matos, A;

Publicação
2021 IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2021

Abstract
To perform autonomous tasks, robots in real-world environments must be able to navigate in dynamic and unknown spaces. To do so, they must recognize previously seen places to compensate for accumulated positional deviations. This task requires effective identification of recovered landmarks to produce a consistent map, and the use of binary descriptors is increasing, especially because of their compact representation. The visual Bag-of-Words (BoW) algorithm is one of the most commonly used techniques to perform appearance-based loop closure detection quickly and robustly. Therefore, this paper presents a behavioral evaluation of a conventional BoW scheme based on Oriented FAST and Rotated BRIEF (ORB) features for image similarity detection in challenging scenarios. For each scenario, full-indexing vocabularies are created to model the operating environment and evaluate the performance for recognizing previously seen places similar to online approaches. Experiments were conducted on multiple public datasets containing scene changes, perceptual aliasing conditions, or dynamic elements. The Bag of Binary Words technique shows a good balance to deal with such severe conditions at a low computational cost. © 2021 IEEE.

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.

Teses
supervisionadas

2020

Underwater 3D reconstruction and reinforcement of machine learning in object recognition

Autor
Alexandra Pereira Nunes

Instituição
UP-FEUP

2020

Modelação e controlo de um sistema robótico multicorpo interconectado

Autor
Gonçalo Gil Seixas Rodrigues de Carvalho

Instituição
UP-FEUP

2020

Modelação e simulação de um sistema robótico multicorpo interconectado

Autor
Gonçalo Gil Seixas Rodrigues de Carvalho

Instituição
UP-FEUP

2020

Adaptive close-range navigation for inspection pf underwater structures

Autor
Ana Rita da Silva Gaspar

Instituição
UP-FEUP

2020

Distributed Perception from Multiple Intelligent Systems for Offshore Marine Surveys

Autor
Daniel Filipe Barros Campos

Instituição
UP-FEUP