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About

About

Concluí o Mestrado Integrado em Engenharia Eletrotécnica e de Computadores na Faculdade de Engenharia da Universidade do Porto, em Fevereiro de 2017. No culminar da minha formação, com a realização da dissertação de mestrado, iniciei a colaboração com o centro de Robótica e Sistemas (CRAS) do INESC TEC. A mesma teve como  objetivo o desenvolvimento de um sistema visual de navegação e mapeamento simultâneos em proximidade ao fundo do mar, com o desenvolvimento de um método de vocabulário visual online para reconhecimento de áreas revisitadas por parte dos veículos subaquáticos autónomos (AUV). Atualmente, desde Maio de 2017, sou bolseira do CRAS. Participei no projeto de um sistema de localização baseado em recetores GPS e sistema inercial e, neste momento, encontro-me envolvida na área de visão e percepção.

 

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Publications

2023

Limit Characterization for Visual Place Recognition in Underwater Scenes

Authors
Gaspar, AR; Nunes, A; Matos, A;

Publication
ROBOT2022: FIFTH IBERIAN ROBOTICS CONFERENCE: ADVANCES IN ROBOTICS, VOL 1

Abstract

2021

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

Authors
Gaspar, AR; Nunes, A; Matos, A;

Publication
2021 IEEE INTERNATIONAL CONFERENCE ON AUTONOMOUS ROBOT SYSTEMS AND COMPETITIONS (ICARSC)

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.

2021

Occupancy Grid Mapping from 2D SONAR Data for Underwater Scenes

Authors
Nunes, A; Gaspar, AR; Matos, A;

Publication
OCEANS 2021: San Diego – Porto

Abstract

2019

A mosaicking technique for object identification in underwater environments

Authors
Nunes, AP; Silva Gaspar, ARS; Pinto, AM; Matos, AC;

Publication
SENSOR REVIEW

Abstract
Purpose: This paper aims to present a mosaicking method for underwater robotic applications, whose result can be provided to other perceptual systems for scene understanding such as real-time object recognition. Design/methodology/approach: This method is called robust and large-scale mosaicking (ROLAMOS) and presents an efficient frame-to-frame motion estimation with outlier removal and consistency checking that maps large visual areas in high resolution. The visual mosaic of the sea-floor is created on-the-fly by a robust registration procedure that composes monocular observations and manages the computational resources. Moreover, the registration process of ROLAMOS aligns the observation to the existing mosaic. Findings: A comprehensive set of experiments compares the performance of ROLAMOS to other similar approaches, using both data sets (publicly available) and live data obtained by a ROV operating in real scenes. The results demonstrate that ROLAMOS is adequate for mapping of sea-floor scenarios as it provides accurate information from the seabed, which is of extreme importance for autonomous robots surveying the environment that does not rely on specialized computers. Originality/value: The ROLAMOS is suitable for robotic applications that require an online, robust and effective technique to reconstruct the underwater environment from only visual information. © 2018, Emerald Publishing Limited.

2019

Simultaneous Underwater Navigation and Mapping

Authors
Gaspar, ARS; Matos, A;

Publication
U.Porto Journal of Engineering

Abstract
The use of underwater autonomous vehicles has been growing, allowing the performance of tasks that cause inherent risks to Human, namely in inspection processes near to structures. With growth in usage of systems with autonomous navigation, visual acquisition methods have also gotten more developed because, they have appealing cost and they also show interesting results when operate at a short distance. It is possible to improve the quality of navigation through visual SLAM techniques which can map and locate simultaneously and its key aspect is the detection of revisited areas. These techniques are not usually applied to underwater scenarios and, therefore, its performance in environment is unknown. The paper presents a more reliable navigation system for underwater vehicles, resorting to some visual SLAM techniques from literature. The results, conducted in a realistic scenario, demonstrated the ability of the system to be applied to underwater environment.