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About

About

Marcelo R. Petry is a robotics researcher and educator in the Centre for Robotics in Industry and Intelligent Systems at INESC TEC (Portugal). His research lies at the intersection of robotics, computer vision, and mixed reality, aiming the application of robots to 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 Assistant Professor at Federal University of Santa Catarina, and researcher at INESC P&D Brazil (2014 to 2019).

Interest
Topics
Details

Details

016
Publications

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 ability 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 volume 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 network 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 quantitative 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. © 2022

2022

Collision avoidance considering iterative Bézier based approach for steep slope terrains

Authors
Santos, LC; Santos, FN; Valente, A; Sobreira, H; Sarmento, J; Petry, M;

Publication
IEEE ACCESS

Abstract

2022

Augmented Reality for Human-Robot Collaboration and Cooperation in Industrial Applications: A Systematic Literature Review

Authors
Costa, GD; Petry, MR; Moreira, AP;

Publication
SENSORS

Abstract
With the continuously growing usage of collaborative robots in industry, the need for achieving a seamless human-robot interaction has also increased, considering that it is a key factor towards reaching a more flexible, effective, and efficient production line. As a prominent and prospective tool to support the human operator to understand and interact with robots, Augmented Reality (AR) has been employed in numerous human-robot collaborative and cooperative industrial applications. Therefore, this systematic literature review critically appraises 32 papers' published between 2016 and 2021 to identify the main employed AR technologies, outline the current state of the art of augmented reality for human-robot collaboration and cooperation, and point out future developments for this research field. Results suggest that this is still an expanding research field, especially with the advent of recent advancements regarding head-mounted displays (HMDs). Moreover, projector-based and HMDs developed approaches are showing promising positive influences over operator-related aspects such as performance, task awareness, and safety feeling, even though HMDs need further maturation in ergonomic aspects. Further research should focus on large-scale assessment of the proposed solutions in industrial environments, involving the solution's target audience, and on establishing standards and guidelines for developing AR assistance systems.

2022

OptiOdom: a Generic Approach for Odometry Calibration of Wheeled Mobile Robots

Authors
Sousa, RB; Petry, MR; Costa, PG; Moreira, AP;

Publication
JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS

Abstract
Odometry calibration adjusts the kinematic parameters or directly the robot's model to improve the wheeled odometry accuracy. The existent literature considers in the calibration procedure only one steering geometry (differential drive, Ackerman/tricycle, or omnidirectional). Our method, the OptiOdom calibration algorithm, generalizes the odometry calibration problem. It is developed an optimization-based approach that uses the improved Resilient Propagation without weight-backtracking (iRprop-) for estimating the kinematic parameters using only the position data of the robot. Even though a calibration path is suggested to be used in the calibration procedure, the OptiOdom method is not path-specific. In the experiments performed, the OptiOdom was tested using four different robots on a square, arbitrary, and suggested calibration paths. The OptiTrack motion capture system was used as a ground-truth. Overall, the use of OptiOdom led to improvements in the odometry accuracy (in terms of maximum distance and absolute orientation errors over the path) over the existent literature while being a generalized approach to the odometry calibration problem. The OptiOdom and the methods from the literature implemented in the article are available in GitHub as an open-source repository.

2021

Autonomous wheelchair for patient’s transportation on healthcare institutions

Authors
Baltazar, AR; Petry, MR; Silva, MF; Moreira, AP;

Publication
SN APPLIED SCIENCES

Abstract
AbstractThe transport of patients from the inpatient service to the operating room is a recurrent task in a hospital routine. This task is repetitive, non-ergonomic, time consuming, and requires the labor of patient transporters. In this paper is presented a system, named Connected Driverless Wheelchair, that can receive transportation requests directly from the hospital information management system, pick up patients at their beds, navigate autonomously through different floors, avoid obstacles, communicate with elevators, and drop patients off at the designated operating room. As a result, a prototype capable of transporting patients autonomously in hospital environments was obtained. Although it was impossible to test the final developed system at the hospital as planned, due to the COVID-19 pandemic, the extensive tests conducted at the robotics laboratory facilities, and our previous experience in integrating mobile robots in hospitals, allowed to conclude that it is perfectly prepared for this integration to be carried out. The achieved results are relevant since this is a system that may be applied to support these types of tasks in the future, making the transport of patients more efficient (both from a cost and time perspective), without unpredictable delays and, in some cases, safer.

Supervised
thesis

2020

Odometry and Extrinsic Sensor Calibration on Mobile Robots

Author
Ricardo Barbosa Sousa

Institution
UP-FEUP

2020

Autonomous Wheelchair to support Patients of Hospital Services

Author
André Rodrigues Baltazar

Institution
UP-FEUP