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About

About

I was born on October 11, 1994 in Vila Nova de Famalicão, Braga, where I still live nowadays.

In 2012, I’ve joined ISEP in the Bachelor’s degree in Electrical Engineering, branch of Electronics and Computers, concluded 3 years later. In the last year of the Bachelor’s I developed the final project in the CRAS laboratory, on ISEP.

After that, in 2015, I started the Master’s degree in Electrical Engineering, branch of Autonomous Systems at the Autonomous Systems Laboratory (ISEP), where I got even more interest in the robotics world.

A year later, August 2016, I started working as a researcher at INESC TEC and I’m being working with aerial robots.

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Details

Details

001
Publications

2019

LiDAR-Based Real-Time Detection and Modeling of Power Lines for Unmanned Aerial Vehicles

Authors
Azevedo, F; Dias, A; Almeida, J; Oliveira, A; Ferreira, A; Santos, T; Martins, A; Silva, E;

Publication
Sensors

Abstract
The effective monitoring and maintenance of power lines are becoming increasingly important due to a global growing dependence on electricity. The costs and risks associated with the traditional foot patrol and helicopter-based inspections can be reduced by using UAVs with the appropriate sensors. However, this implies developing algorithms to make the power line inspection process reliable and autonomous. In order to overcome the limitations of visual methods in the presence of poor light and noisy backgrounds, we propose to address the problem of power line detection and modeling based on LiDAR. The PL 2 DM, Power Line LiDAR-based Detection and Modeling, is a novel approach to detect power lines. Its basis is a scan-by-scan adaptive neighbor minimalist comparison for all the points in a point cloud. The power line final model is obtained by matching and grouping several line segments, using their collinearity properties. Horizontally, the power lines are modeled as a straight line, and vertically as a catenary curve. Using a real dataset, the algorithm showed promising results both in terms of outputs and processing time, adding real-time object-based perception capabilities for other layers of processing.

2019

Real- Time LiDAR-based Power Lines Detection for Unmanned Aerial Vehicles

Authors
Azevedo, F; Dias, A; Almeida, J; Oliveira, A; Ferreira, A; Santos, T; Martins, A; Silva, E;

Publication
19th IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2019

Abstract
The growing dependence of modern-day societies on electricity increases the importance of effective monitoring and maintenance of power lines. Endowing UAVs with the appropriate sensors for inspecting power lines, the costs and risks associated with the traditional foot patrol and helicopter-based inspections can be reduced. However, this implies the development of algorithms to make the inspection process reliable and autonomous. Visual methods are usually applied to locate the power lines and their components, but poor light conditions or a background rich in edges may compromise their results. To overcome those limitations, we propose to address the problem of power line detection and modeling based on LiDAR. A novel approach to the power line detection was developed, the PL2DM - Power Line LiDAR-based Detection and Modeling. It is based in a scan-by-scan adaptive neighbor minimalist comparison for all the points in a point cloud. The power line final model is obtained by matching and grouping several line segments, using their collinearity properties. Horizontally, the power lines are modeled as a straight line, and vertically as a catenary curve. The algorithm was validated with a real dataset, showing promising results both in terms of outputs and processing time, adding real-time object-based perception capabilities for other layers of processing. © 2019 IEEE.

2017

Collision avoidance for safe structure inspection with multirotor UAV

Authors
Azevedo, F; Oliveira, A; Dias, A; Almeida, J; Moreira, M; Santos, T; Ferreira, A; Martins, A; Silva, E;

Publication
2017 European Conference on Mobile Robots (ECMR)

Abstract