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About

Hi!

I'm a senior researcher at Centre for Robotics and Autonomous Systems (CRAS) at INESC TEC.

I have received a MS.c. degree in Electrical and Computer Engineering from the Faculty of Engineering of the University of Porto (FEUP) in 2005, and a Ph.D. from the Department of Electrical and Computer Engineering of FEUP, Portugal, in 2014.

I'm being involved in several R&D projects for the last 7 years (related with mobile robotic, intelligent systems and autonomous platforms) as well as in some partnerhips with the industry. Moreover, I'm the principal author of several articles in top-ranked journals of robotics and computer vision.

Currently, my research interests include artificial intelligence, robotics, visual motion perception, motion analysis, optical flow, unsupervised segmentation, 3D reconstructions and underwater imaging.

Interest
Topics
Details

Details

006
Publications

2020

MARESye: A hybrid imaging system for underwater robotic applications

Authors
Pinto, AM; Matos, AC;

Publication
INFORMATION FUSION

Abstract
This article presents an innovative hybrid imaging system that provides dense and accurate 3D information from harsh underwater environments. The proposed system is called MARESye and captures the advantages of both active and passive imaging methods: multiple light stripe range (LSR) and a photometric stereo (PS) technique, respectively. This hybrid approach fuses information from these techniques through a data-driven formulation to extend the measurement range and to produce high density 3D estimations in dynamic underwater environments. This hybrid system is driven by a gating timing approach to reduce the impact of several photometric issues related to the underwater environments such as, diffuse reflection, water turbidity and non-uniform illumination. Moreover, MARESye synchronizes and matches the acquisition of images with sub-sea phenomena which leads to clear pictures (with a high signal-to-noise ratio). Results conducted in realistic environments showed that MARESye is able to provide reliable, high density and accurate 3D data. Moreover, the experiments demonstrated that the performance of MARESye is less affected by sub-sea conditions since the SSIM index was 0.655 in high turbidity waters. Conventional imaging techniques obtained 0.328 in similar testing conditions. Therefore, the proposed system represents a valuable contribution for the inspection of maritime structures as well as for the navigation procedures of autonomous underwater vehicles during close range operations.

2020

Dense disparity maps from rgb and sparse depth information using deep regression models

Authors
Leite, PN; Silva, RJ; Campos, DF; Pinto, AM;

Publication
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Abstract
A dense and accurate disparity map is relevant for a large number of applications, ranging from autonomous driving to robotic grasping. Recent developments in machine learning techniques enable us to bypass sensor limitations, such as low resolution, by using deep regression models to complete otherwise sparse representations of the 3D space. This article proposes two main approaches that use a single RGB image and sparse depth information gathered from a variety of sensors/techniques (stereo, LiDAR and Light Stripe Ranging (LSR)): a Convolutional Neural Network (CNN) and a cascade architecture, that aims to improve the results of the first. Ablation studies were conducted to infer the impact of these depth cues on the performance of each model. The models trained with LiDAR sparse information are the most reliable, achieving an average Root Mean Squared Error (RMSE) of 11.8 cm on our own Inhouse dataset; while the LSR proved to be too sparse of an input to compute accurate predictions on its own. © Springer Nature Switzerland AG 2020.

2020

Multi-agent optimization for offshore wind farm inspection using an improved population-based metaheuristic

Authors
Silva, RJ; Leite, PN; Pinto, AM;

Publication
2020 IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2020

Abstract
The use of robotic solutions in tasks such as the inspection and monitorization of offshore wind farms aims to, not only mitigate the involved risks, but also to reduce the costs of operating and maintaining these structures. Performing a complete inspection of the platforms in useful time is crucial. Therefore, multiple agents can prove to be a cost-effective solution. This work proposes a trajectory planning algorithm, based on the Ant Colony metaheuristic, capable of optimizing the number of Autonomous Surface Vehicles (ASVs) to be used, and their corresponding route.Experiments conducted on a simulated environment, representative of the real scenario, proves this approach to be successful in planning a trajectory that is able to select the appropriate number of agents and the trajectory of each agent that avoids collisions and at the same time guarantees the full observation of the offshore structures. © 2020 IEEE.

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

An Hierarchical Architecture for Docking Autonomous Surface Vehicles

Authors
Leite, P; Silva, R; Matos, A; Pinto, AM;

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

Abstract
Autonomous Surface Vehicles (ASVs) provide the ideal platform to further explore the many opportunities in the cargo shipping industry, by making it more profitable and safer. This paper presents an architecture for the autonomous docking operation, formed by two stages: a maneuver module and, a situational awareness system to detect a mooring facility where an ASV can safely dock. Information retrieved from a 3D LIDAR, IMU and GPS are combined to extract the geometric features of the floating platform and to estimate the relative positioning and orientation of the moor to the ASV. Then, the maneuver module plans a trajectory to a specific position and guarantees that the ASV will not collide with the mooring facility. The approach presented in this paper was validated in distinct environmental and weather conditions such as tidal waves and wind. The results demonstrate the ability of the proposed architecture for detecting the docking platform and safely conduct the navigation towards it, achieving errors up to 0.107 m in position and 6.58 degrees in orientation.

Supervised
thesis

2019

A Self-Guided Docking Architecture for Autonomous Surface Vehicles

Author
Pedro Nuno Barbosa Leite

Institution
UP-FEUP

2018

Distributed Perception using Multiple Autonomous Vehicles for Marine Asset Inspection

Author
Daniel Filipe Barros Campos

Institution
UP-FEUP

2018

Seguimento multisensorial de objetos dinâmicos para aplicações robóticas

Author
Miguel Alexandre Vilela Bertão

Institution
UP-FEUP

2018

Safety features for unmanned maritime vehicles

Author
André Manuel Matos Leite

Institution
UP-FEUP

2018

Altitude Control of an Underwatervehicle Based on Computer Vision

Author
Pedro Miguel Flores Rodrigues

Institution
UP-FEUP