Cookies
O website necessita de alguns cookies e outros recursos semelhantes para funcionar. Caso o permita, o INESC TEC irá utilizar cookies para recolher dados sobre as suas visitas, contribuindo, assim, para estatísticas agregadas que permitem melhorar o nosso serviço. Ver mais
Aceitar Rejeitar
  • Menu
Sobre

Sobre

Olá! Sou investigador do Centro de Robótica e Sistemas Autónomos (CRAS) no INESC TEC com uma bolsa de doutoramento da FCT. Recebi o meu mestrado em Engenharia Eletrotécnica e de Computadores na Faculdade de Engenharia da Universidade do Porto (FEUP), Portugal, em 2014. Desde então, estive envolvido em diversos projetos de I&D relacionados com o desenvolvimento de robôs de serviços e industriais, tanto ao nível empresarial como de investigação. Em 2018, decidi candidatar-me ao Programa Doutoral de Engenharia Eletrotécnica e de Computadores na Faculdade de Engenharia da Universidade do Porto (FEUP), Portugal, começando a minha colaboração com o CRAS. Atualmente, as minhas atividade de investigação incluem robótica, reconstruções 3D multidomínio do ambiente, perceção distribuída e desvio de obstáculos principalmente aplicados ao ambiente marítimo. Para mais informações consulte o meu CV em: https://cienciavitae.pt/portal/pt/661B-6DD9-0B87

Tópicos
de interesse
Detalhes

Detalhes

002
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

Automatic Program Repair as Semantic Suggestions: An Empirical Study

Autores
Campos, D; Restivo, A; Ferreira, HS; Ramos, A;

Publicação
2021 14TH IEEE CONFERENCE ON SOFTWARE TESTING, VERIFICATION AND VALIDATION (ICST 2021)

Abstract

2021

ATLANTIS - The Atlantic Testing Platform for Maritime Robotics

Autores
Pinto A.M.; Marques J.V.A.; Campos D.F.; Abreu N.; Matos A.; Jussi M.; Berglund R.; Halme J.; Tikka P.; Formiga J.; Verrecchia C.; Langiano S.; Santos C.; Sa N.; Stoker J.J.; Calderoni F.; Govindaraj S.; But A.; Gale L.; Ribas D.; Hurtos N.; Vidal E.; Ridao P.; Chieslak P.; Palomeras N.; Barberis S.; Aceto L.;

Publicação
Oceans Conference Record (IEEE)

Abstract

2021

DIIUS - Distributed Perception for Inspection of Aquatic Structures

Autores
Campos D.F.; Pereira M.; Matos A.; Pinto A.M.;

Publicação
Oceans Conference Record (IEEE)

Abstract
The worldwide context has fostered the innovation geared to the blue growth. However, the aquatic environment imposes many restrictions to mobile robots, as their perceptual capacity becomes severely limited. DIIUS aims to strengthen the perception of distributed robotic systems to improve the current procedures for inspection of aquatic structures (constructions and/or vessels).The perception of large working areas from multiples robots raises a number of unresolved inference problems and calls for new interaction patterns between multiple disciplines, both at the conceptual and technical level. To address this important challenge, the DIIUS project seeks to reinforce the current state-of-art in several scientific domains that fit into artificial intelligence, computer vision, and robotics. Through case studies focused on 3D mapping of aquatic structures (ex., maritime constructions and adduction tunnels), the project investigates new spatio-temporal data association techniques, including the correlation of sensors from heterogeneous robot formations operating in environments with communications constraints.

2020

Dense disparity maps from rgb and sparse depth information using deep regression models

Autores
Leite, PN; Silva, RJ; Campos, DF; Pinto, AM;

Publicação
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Abstract
A dense and accurate disparity map is relevant for a large number of applications, ranging from autonomous driving to robotic grasping. Recent developments in machine learning techniques enable us to bypass sensor limitations, such as low resolution, by using deep regression models to complete otherwise sparse representations of the 3D space. This article proposes two main approaches that use a single RGB image and sparse depth information gathered from a variety of sensors/techniques (stereo, LiDAR and Light Stripe Ranging (LSR)): a Convolutional Neural Network (CNN) and a cascade architecture, that aims to improve the results of the first. Ablation studies were conducted to infer the impact of these depth cues on the performance of each model. The models trained with LiDAR sparse information are the most reliable, achieving an average Root Mean Squared Error (RMSE) of 11.8 cm on our own Inhouse dataset; while the LSR proved to be too sparse of an input to compute accurate predictions on its own. © Springer Nature Switzerland AG 2020.