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About

I am a Senior Researcher at the center for Robotics and Autonomous Systems at INESC TEC. I graduated in Electrical and Computer Engineering from the Faculty of Engineering of the University of Porto, first with a MSc degree in 2009 and with a PhD degree in 2014. Since 2009, I have been working on Surface and Underwater Robotics, researching on Control, Guidance, Localization and Coordination of marine robots.

My activities have been developed in the context of several national and international projects, among which the following are highlighted: Lajeado (development of an AUV for dam inspection); FP7 ICARUS (Integrated Components for Assisted Rescue and Unmanned Search operations); and FLEXUS (Flexible Unmanned Surface vehicles for the Internet of moving things), funded by H2020 RAWFIE project.

I am also involved in the development of several robotic systems and at the origin of several prototypes such as the autonomous surface vehicle FLEXUS and the autonomous underwater vehicle SHAD.

Interest
Topics
Details

Details

011
Publications

2021

A Performance Analysis of Feature Extraction Algorithms for Acoustic Image-Based Underwater Navigation

Authors
Oliveira, AJ; Ferreira, BM; Cruz, NA;

Publication
Journal of Marine Science and Engineering

Abstract
In underwater navigation, sonars are useful sensing devices for operation in confined or structured environments, enabling the detection and identification of underwater environmental features through the acquisition of acoustic images. Nonetheless, in these environments, several problems affect their performance, such as background noise and multiple secondary echoes. In recent years, research has been conducted regarding the application of feature extraction algorithms to underwater acoustic images, with the purpose of achieving a robust solution for the detection and matching of environmental features. However, since these algorithms were originally developed for optical image analysis, conclusions in the literature diverge regarding their suitability to acoustic imaging. This article presents a detailed comparison between the SURF (Speeded-Up Robust Features), ORB (Oriented FAST and Rotated BRIEF), BRISK (Binary Robust Invariant Scalable Keypoints), and SURF-Harris algorithms, based on the performance of their feature detection and description procedures, when applied to acoustic data collected by an autonomous underwater vehicle. Several characteristics of the studied algorithms were taken into account, such as feature point distribution, feature detection accuracy, and feature description robustness. A possible adaptation of feature extraction procedures to acoustic imaging is further explored through the implementation of a feature selection module. The performed comparison has also provided evidence that further development of the current feature description methodologies might be required for underwater acoustic image analysis.

2020

UDMSim: A Simulation Platform for Underwater Data Muling Communications

Authors
Teixeira, FB; Moreira, N; Abreu, N; Ferreira, B; Ricardo, M; Campos, R;

Publication
16th International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2020, Thessaloniki, Greece, October 12-14, 2020

Abstract
The use of Autonomous Underwater Vehicles (AUVs) is increasingly seen as a cost-effective way to carry out underwater missions. Due to their long endurance and set of sensors onboard, AUVs may collect large amounts of data, in the order of Gbytes, which need to be transferred to shore. State of the art wireless technologies suffer either from low bitrates or limited range. Since surfacing may be unpractical, especially for deep sea operations, long-range underwater data transfer is limited to the use of low bitrate acoustic communications, precluding the timely transmission of large amounts of data. The use of data mules combined with short-range, high bitrate RF or optical communications has been proposed as a solution to overcome the problem.In this paper we describe the implementation and validation of UDMSim, a simulation platform for underwater data muling oriented systems that combines an AUV simulator and the Network Simulator 3 (ns-3). The results presented in this paper show a good match between UDMSim, a theoretical model, and the experimental results obtained by using an underwater testbed when no localization errors exist. When these errors are present, the simulator is able to reproduce the navigation of AUVs that act as data mules, adjust the throughput, and simulate the signal and connection losses that the theoretical model can not predict, but that will occur in reality. UDMSim is made available to the community to support easy and faster evaluation of data muling oriented underwater communications solutions, and enable offline replication of real world experiments. © 2020 IEEE.

2020

Cross-Sensor Quality Assurance for Marine Observatories

Authors
Diamant, R; Shachar, I; Makovsky, Y; Ferreira, BM; Cruz, NA;

Publication
REMOTE SENSING

Abstract
Measuring and forecasting changes in coastal and deep-water ecosystems and climates requires sustained long-term measurements from marine observation systems. One of the key considerations in analyzing data from marine observatories is quality assurance (QA). The data acquired by these infrastructures accumulates into Giga and Terabytes per year, necessitating an accurate automatic identification of false samples. A particular challenge in the QA of oceanographic datasets is the avoidance of disqualification of data samples that, while appearing as outliers, actually represent real short-term phenomena, that are of importance. In this paper, we present a novel cross-sensor QA approach that validates the disqualification decision of a data sample from an examined dataset by comparing it to samples from related datasets. This group of related datasets is chosen so as to reflect upon the same oceanographic phenomena that enable some prediction of the examined dataset. In our approach, a disqualification is validated if the detected anomaly is present only in the examined dataset, but not in its related datasets. Results for a surface water temperature dataset recorded by our Texas A&M-Haifa Eastern Mediterranean Marine Observatory (THEMO)-over a period of 7 months, show an improved trade-off between accurate and false disqualification rates when compared to two standard benchmark schemes.

2019

REX 16-Robotic Exercises 2016 Multi-robot field trials

Authors
Marques, MM; Mendonca, R; Marques, F; Ramalho, T; Lobo, V; Matos, A; Ferreira, B; Simoes, N; Castelao, I;

Publication
2019 IEEE UNDERWATER TECHNOLOGY (UT)

Abstract
Nowadays, one of the problems associated with Unmanned Systems is the gap between research community and end-users. In order to emend this problem, the Portuguese Navy Research Center (CINAV) conducts the REX 2016 (Robotic Exercises). This paper describes the trials that were presented in this exercise, divided in two phases. The first phase happened at the Naval Base in Lisbon, with the support of divers and RHIBs (Rigid-Hulled Inflatable Boats), and the second phase, also with divers' support, at the coast of Lisbon-Cascais. It counted with many participants and research groups, including INESC-TEC, UNINOVA, TEKEVER and UAVISION. There are several advantages of doing this exercise, including for the Portuguese Navy, but also for partners. For the Navy, because it is an opportunity of being in contact with recent market technologies and researches. On the other hand, it is an opportunity for the partners to test their systems in a real environment, which usually is a difficult action to accomplish. Therefore, the paper describes three of the most relevant experiments: underwater docking stations, UAV and USV cooperation and Tracking targets from UAVs.

2019

Experimental evaluation of segmentation algorithms for corner detection in sonar images

Authors
Oliveira, PL; Ferreira, BM; Cruz, NA;

Publication
OCEANS 2019 MTS/IEEE Seattle, OCEANS 2019

Abstract
Corners usually appear very distinct from the rest of the scene in a mechanical scanning imaging sonar (MSIS) image, generally characterized by sharp intensities. The detection of corners is particularly useful in human-structured environments such as tanks because the knowledge on their location provides a way to compute the vehicle position. The combination of some basic operations typically used for image segmentation have great potential to detect and localize corners in sonar images automatically. This article proposes and evaluates with experimental data a set of image segmentation algorithms for corner detection in sonar scans. The developed algorithms are evaluated with ground truth, and their performance is analyzed following a few relevant metrics for autonomous navigation. © 2019 Marine Technology Society.

Supervised
thesis

2020

Information-aware Feature-based Underwater Localization and Motion Planning

Author
António José Ventura de Oliveira

Institution
UP-FEUP

2020

Control of an Autonomous Underwater Vehicle in 6 Degrees of Freedom

Author
José Francisco Saraiva Santos

Institution
UP-FEUP

2020

Guidance of an Autonomous Surface Vehicle for Underwater Navigation Aid

Author
José Pedro Martins Pires e Sousa

Institution
UP-FEUP

2020

Feature-based underwater localization using an imaging sonar

Author
António José Ventura de Oliveira

Institution
UP-FEUP

2020

Underwater mapping using a SONAR

Author
João Pedro Bastos Fula

Institution
UP-FEUP