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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

020
Publications

2021

Particle filter refinement based on clustering procedures for high-dimensional localization and mapping systems

Authors
Aguiar, AS; dos Santos, FN; Sobreira, H; Cunha, JB; Sousa, AJ;

Publication
Robotics and Autonomous Systems

Abstract

2020

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

2020

Development of an Autonomous Mobile Towing Vehicle for Logistic Tasks

Authors
Rocha, C; Sousa, I; Ferreira, F; Sobreira, H; Lima, J; Veiga, G; Moreira, AP;

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

Abstract

2020

Localization and Mapping for Robots in Agriculture and Forestry: A Survey

Authors
Aguiar, AS; dos Santos, FN; Cunha, JB; Sobreira, H; Sousa, AJ;

Publication
Robotics

Abstract
Research and development of autonomous mobile robotic solutions that can perform several active agricultural tasks (pruning, harvesting, mowing) have been growing. Robots are now used for a variety of tasks such as planting, harvesting, environmental monitoring, supply of water and nutrients, and others. To do so, robots need to be able to perform online localization and, if desired, mapping. The most used approach for localization in agricultural applications is based in standalone Global Navigation Satellite System-based systems. However, in many agricultural and forest environments, satellite signals are unavailable or inaccurate, which leads to the need of advanced solutions independent from these signals. Approaches like simultaneous localization and mapping and visual odometry are the most promising solutions to increase localization reliability and availability. This work leads to the main conclusion that, few methods can achieve simultaneously the desired goals of scalability, availability, and accuracy, due to the challenges imposed by these harsh environments. In the near future, novel contributions to this field are expected that will help one to achieve the desired goals, with the development of more advanced techniques, based on 3D localization, and semantic and topological mapping. In this context, this work proposes an analysis of the current state-of-the-art of localization and mapping approaches in agriculture and forest environments. Additionally, an overview about the available datasets to develop and test these approaches is performed. Finally, a critical analysis of this research field is done, with the characterization of the literature using a variety of metrics.

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

Supervised
thesis

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

2016

Sistema de navegação para plataforma móvel omnidirecional

Author
Fernando Jorge Marques de Sá

Institution
UP-FEUP