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About

I was born in  Leiria, Portugal, in July, 1985. I graduated with a M.Sc. degree in Electrical Engineering from the University of Porto in 2009. Since then, I have been developing my research within the Robotic and Intelligent Systems Unit of INESC-Porto (the Institute for Systems and Computer Engineering of Porto). My main research area is navigation and control of indoor autonomous vehicles.

Interest
Topics
Details

Details

010
Publications

2019

Map-Matching Algorithms for Robot Self-Localization: A Comparison Between Perfect Match, Iterative Closest Point and Normal Distributions Transform

Authors
Sobreira, H; Costa, CM; Sousa, I; Rocha, L; Lima, J; Farias, PCMA; Costa, P; Paulo Moreira, AP;

Publication
Journal of Intelligent and Robotic Systems: Theory and Applications

Abstract
The self-localization of mobile robots in the environment is one of the most fundamental problems in the robotics navigation field. It is a complex and challenging problem due to the high requirements of autonomous mobile vehicles, particularly with regard to the algorithms accuracy, robustness and computational efficiency. In this paper, we present a comparison of three of the most used map-matching algorithms applied in localization based on natural landmarks: our implementation of the Perfect Match (PM) and the Point Cloud Library (PCL) implementation of the Iterative Closest Point (ICP) and the Normal Distribution Transform (NDT). For the purpose of this comparison we have considered a set of representative metrics, such as pose estimation accuracy, computational efficiency, convergence speed, maximum admissible initialization error and robustness to the presence of outliers in the robots sensors data. The test results were retrieved using our ROS natural landmark public dataset, containing several tests with simulated and real sensor data. The performance and robustness of the Perfect Match is highlighted throughout this article and is of paramount importance for real-time embedded systems with limited computing power that require accurate pose estimation and fast reaction times for high speed navigation. Moreover, we added to PCL a new algorithm for performing correspondence estimation using lookup tables that was inspired by the PM approach to solve this problem. This new method for computing the closest map point to a given sensor reading proved to be 40 to 60 times faster than the existing k-d tree approach in PCL and allowed the Iterative Closest Point algorithm to perform point cloud registration 5 to 9 times faster. © 2018 Springer Science+Business Media B.V., part of Springer Nature

2019

New Approach to Supervise Localization Algorithms

Authors
Coelho, FD; Guedes, PM; Guimaraes, DA; Sobreira, HM; Moreira, AP;

Publication
2019 19TH IEEE INTERNATIONAL CONFERENCE ON AUTONOMOUS ROBOT SYSTEMS AND COMPETITIONS (ICARSC 2019)

Abstract
The localization algorithms have different errors which can impair the robot's navigation. In this way, we propose an approach that will supervise the localization while the robot navigate. Our approach is based on another work present in the literature, where we detected a problem during its analysis. Therefore, this article will present a new method based on the RLS algorithm, to solve the identified problem. Besides, we propose the supervision of two more localization algorithms, being now four the supervised algorithms, namely: Augmented Monte Carlo Localization, Extended Kalman Filter with Beacons, Perfect Match and Odometry. The results show that the robustness and reliability of the system were increased.

2019

New Approach to Supervise Localization Algorithms

Authors
De Oliveira Coelho, F; Guedes, PM; Guimarães, DA; Sobreira, HM; Moreira, AP;

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

Abstract
The localization algorithms have different errors which can impair the robot's navigation. In this way, we propose an approach that will supervise the localization while the robot navigate. Our approach is based on another work present in the literature, where we detected a problem during its analysis. Therefore, this article will present a new method based on the RLS algorithm, to solve the identified problem. Besides, we propose the supervision of two more localization algorithms, being now four the supervised algorithms, namely: Augmented Monte Carlo Localization, Extended Kalman Filter with Beacons, Perfect Match and Odometry. The results show that the robustness and reliability of the system were increased. © 2019 IEEE.

2019

Autonomous Robot Navigation for Automotive Assembly Task: An Industry Use-Case

Authors
Sobreira, H; Rocha, L; Lima, J; Rodrigues, F; Moreira, AP; Veiga, G;

Publication
Advances in Intelligent Systems and Computing - Robot 2019: Fourth Iberian Robotics Conference

Abstract

2018

Landmark detection for docking tasks

Authors
Ferreira, F; Sobreira, H; Veiga, G; Moreira, A;

Publication
Advances in Intelligent Systems and Computing

Abstract
For docking manoeuvres, the detection of the objects to dock needs to be precise as the minimum deviation from the objective may lead to the failure of this task. The objective of this article is to test possible ways to detect a landmark using a laser rangefinder for docking manoeuvres. We will test a beacon-based localisation algorithm and an algorithm based on natural landmarks already implemented, however, we will apply modifications to such methods. To verify the possibility of docking using these methods, we will conduct experiments with a real robot. © Springer International Publishing AG 2018.

Supervised
thesis

2016

Sistema de navegação para plataforma móvel omnidirecional

Author
Fernando Jorge Marques de Sá

Institution
UP-FEUP

2016

Plataforma robótica genérica para robô de logística, serviços ou vigilância com mecanismo de troca automática da bateria

Author
Ivo Emanuel Milheiro de Sousa

Institution
UP-FEUP