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About

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

001
Publications

2018

Comparative Study of Visual Odometry and SLAM Techniques

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

Publication
Advances in Intelligent Systems and Computing

Abstract
The use of the odometry and SLAM visual methods in autonomous vehicles has been growing. Optical sensors provide valuable information from the scenario that enhance the navigation of autonomous vehicles. Although several visual techniques are already available in the literature, their performance could be significantly affected by the scene captured by the optical sensor. In this context, this paper presents a comparative analysis of three monocular visual odometry methods and three stereo SLAM techniques. The advantages, particularities and performance of each technique are discussed, to provide information that is relevant for the development of new research and novel robotic applications. © Springer International Publishing AG 2018.

2018

Urban@CRAS dataset: Benchmarking of visual odometry and SLAM techniques

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

Publication
Robotics and Autonomous Systems

Abstract

2018

A Safety Monitoring Model for a Faulty Mobile Robot

Authors
Leite, A; Pinto, A; Matos, A;

Publication
ROBOTICS

Abstract
The continued development of mobile robots (MR) must be accompanied by an increase in robotics' safety measures. Not only must MR be capable of detecting and diagnosing faults, they should also be capable of understanding when the dangers of a mission, to themselves and the surrounding environment, warrant the abandonment of their endeavors. Analysis of fault detection and diagnosis techniques helps shed light on the challenges of the robotic field, while also showing a lack of research in autonomous decision-making tools. This paper proposes a new skill-based architecture for mobile robots, together with a novel risk assessment and decision-making model to overcome the difficulties currently felt in autonomous robot design.

2017

A Fast and Robust Kinematic Model for a 12 DoF Hyper-Redundant Robot Positioning: an Optimization Proposal

Authors
Lima, J; Pereira, AI; Costa, P; Pinto, A; Costa, P;

Publication
PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON NUMERICAL ANALYSIS AND APPLIED MATHEMATICS 2016 (ICNAAM-2016)

Abstract
This paper describes an optimization procedure for a robot with 12 degrees of freedom avoiding the inverse kinematics problem, which is a hard task for this type of robot manipulator. This robot can be used to pick and place tasks in complex designs. Combining an accurate and fast direct kinematics model with optimization strategies, it is possible to achieve the joints angles for a desired end-effector position and orientation. The optimization methods stretched simulated annealing algorithm and genetic algorithm were used. The solutions found were validated using data originated by a real and by a simulated robot formed by 12 servomotors with a gripper.

2017

Visual motion perception for mobile robots through dense optical flow fields

Authors
Pinto, AM; Costa, PG; Correia, MV; Matos, AC; Moreira, AP;

Publication
ROBOTICS AND AUTONOMOUS SYSTEMS

Abstract
Recent advances in visual motion detection and interpretation have made possible the rising of new robotic systems for autonomous and active surveillance. In this line of research, the current work discusses motion perception by proposing a novel technique that analyzes dense flow fields and distinguishes several regions with distinct motion models. The method is called Wise Optical Flow Clustering (WOFC) and extracts the moving objects by performing two consecutive operations: evaluating and resetting. Motion properties of the flow field are retrieved and described in the evaluation phase, which provides high level information about the spatial segmentation of the flow field. During the resetting operation, these properties are combined and used to feed a guided segmentation approach. The WOFC requires information about the number of motion models and, therefore, this paper introduces a model selection method based on a Bayesian approach that balances the model's fitness and complexity. It combines the correlation of a histogram-based analysis with the decay ratio of the normalized entropy criterion. This approach interprets the flow field and gives an estimative about the number of moving objects. The experiments conducted in a realistic environment have proved that the WOFC presents several advantages that meet the requirements of common robotic and surveillance applications: is computationally efficient and provides a pixel-wise segmentation, comparatively to other state-of-the-art methods.

Supervised
thesis

2016

Inspeção e verificação da correta assemblagem/combinação de peças em linhas de produção

Author
Ricardo Ferreira da Silva

Institution
UP-FEUP

2016

Sistema de monitorização remota e interface de operações para um Robô de Minas e Armadilhas

Author
João Álvaro Silva Costa e Sá

Institution
UP-FEUP

2016

Laser Triangulation System for Measuring Underwater Structures with High Definition

Author
João Miguel Pesqueira Gaspar Pombo

Institution
UP-FEUP

2016

Underwater Sea-floor Mapping for a Robotic Application, Using Visual Information

Author
Henrique Luís dos Santos Pinto

Institution
UP-FEUP