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About

About

Marcelo R. Petry is a robotics researcher and educator at the Centre for Robotics in Industry and Intelligent Systems at INESC TEC (Portugal). His research lies at the intersection of robotics, computer vision, and extended reality, aiming at the application of robots in manufacturing, logistics, inspection, and human assistance. Marcelo graduated in Control and Automation Engineering from the Pontifical Catholic University of Rio Grande do Sul in 2008 (Brazil) and obtained his PhD in Informatics Engineering from the University of Porto in 2013 (Portugal). Previously, he was an Assistant Professor at the Federal University of Santa Catarina and a researcher at INESC P&D Brazil (2014 to 2019).

Interest
Topics
Details

Details

  • Name

    Marcelo Petry
  • Role

    Senior Researcher
  • Since

    04th January 2010
020
Publications

2024

Assessment of Multiple Fiducial Marker Trackers on Hololens 2

Authors
Costa, GM; Petry, MR; Martins, JG; Moreira, AP;

Publication
IEEE Access

Abstract

2023

Comparison of 3D Sensors for Automating Bolt-Tightening Operations in the Automotive Industry

Authors
Dias, J; Simoes, P; Soares, N; Costa, CM; Petry, MR; Veiga, G; Rocha, LF;

Publication
SENSORS

Abstract
Machine vision systems are widely used in assembly lines for providing sensing abilities to robots to allow them to handle dynamic environments. This paper presents a comparison of 3D sensors for evaluating which one is best suited for usage in a machine vision system for robotic fastening operations within an automotive assembly line. The perception system is necessary for taking into account the position uncertainty that arises from the vehicles being transported in an aerial conveyor. Three sensors with different working principles were compared, namely laser triangulation (SICK TriSpector1030), structured light with sequential stripe patterns (Photoneo PhoXi S) and structured light with infrared speckle pattern (Asus Xtion Pro Live). The accuracy of the sensors was measured by computing the root mean square error (RMSE) of the point cloud registrations between their scans and two types of reference point clouds, namely, CAD files and 3D sensor scans. Overall, the RMSE was lower when using sensor scans, with the SICK TriSpector1030 achieving the best results (0.25 mm +/- 0.03 mm), the Photoneo PhoXi S having the intermediate performance (0.49 mm +/- 0.14 mm) and the Asus Xtion Pro Live obtaining the higher RMSE (1.01 mm +/- 0.11 mm). Considering the use case requirements, the final machine vision system relied on the SICK TriSpector1030 sensor and was integrated with a collaborative robot, which was successfully deployed in an vehicle assembly line, achieving 94% success in 53,400 screwing operations.

2023

Assessment of the influence of magnetic perturbations and dynamic motions in a commercial AHRS

Authors
Martins, JG; Petry, MR; Moreira, AP;

Publication
IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2023, Tomar, Portugal, April 26-27, 2023

Abstract

2023

Assessment of the influence of magnetic perturbations and dynamic motions in a commercial AHRS

Authors
Martins, JG; Petry, MR; Moreira, AP;

Publication
2023 IEEE INTERNATIONAL CONFERENCE ON AUTONOMOUS ROBOT SYSTEMS AND COMPETITIONS, ICARSC

Abstract
The pose estimation of a mobile robotic system is essential in many autonomous applications. Inertial sensors provide high-frequency measurements that can be used to estimate the displacement, however, for estimating the orientation, an additional filter is required. Some of the newest Attitude and Heading Reference Systems can provide a referenced estimation of the orientation of the device, allowing it to retrieve the orientation of a robotic system. However, magnetic field perturbations caused by ferromagnetic objects or induced magnetic fields might influence these systems and, consequently, lead to the accumulation of errors over time. In this paper, the performance of the Xsens fusion filter is compared with a stateof-the-art algorithm to estimate the orientation of the system under dynamic movements and in the presence of magnetic perturbations, with the goal of finding the most suitable for an Unmanned Aerial Vehicle. The results show that both filters are robust and perform well in the target scenario, with a root mean squared error between 2 and 5 degrees; however, the Xsens fusion filter does not require an extra computer to process the data.

2022

Deformable convolutions in multi-view stereo

Authors
Masson, JEN; Petry, MR; Coutinho, DF; Honorio, LD;

Publication
IMAGE AND VISION COMPUTING

Abstract
The Multi-View Stereo (MVS) is a key process in the photogrammetry workflow. It is responsible for taking the camera's views and finding the maximum number of matches between the images yielding a dense point cloud of the observed scene. Since this process is based on the matching between images it greatly depends on the abil-ity of features matching throughout different images. To improve the matching performance several researchers have proposed the use of Convolutional Neural Networks (CNNs) to solve the MVS problem. Despite the progress in the MVS problem with the usage of CNNs, the Video RAM (VRAM) consumption within these approaches is usually far greater than classical methods, that rely more on RAM, which is cheaper to expand than VRAM. This work then follows the progress made in CasMVSNet in the reduction of GPU memory usage, and further study the changes in the feature extraction process. The Average Group-wise Correlation is used in the cost vol-ume generation, to reduce the number of channels in the cost volume, yielding a reduction in GPU memory usage without noticeable penalties in the result. The deformable convolutions are applied in the feature extraction net -work to augment the spatial sampling locations with learning offsets, without additional supervision, to further improve the network's ability to model transformations. The impact of these changes is measured using quanti-tative and qualitative tests using the DTU and the Tanks and Temples datasets. The modifications reduced the GPU memory usage by 32% and improved the completeness by 9% with a penalty of 6.6% in accuracy on the DTU dataset.(c) 2021 Published by Elsevier B.V.

Supervised
thesis

2022

Evaluation of the influence and impacts of an augmented reality application as a tool to support production in the context of industry 4.0

Author
Gabriel de Moura Costa

Institution
UP-FEUP

2020

Autonomous Wheelchair to support Patients of Hospital Services

Author
André Rodrigues Baltazar

Institution
UP-FEUP

2020

Odometry and Extrinsic Sensor Calibration on Mobile Robots

Author
Ricardo Barbosa Sousa

Institution
UP-FEUP