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About

BERNARDO TEIXEIRA received the M.Sc. degree in electrical and computer engineering from the Faculdade de Engenharia, Universidade do Porto (FEUP), in 2019. He is currently a Researcher with INESC TEC, Centre for Robotics and Autonomous Systems (CRAS). His research activities pertain mostly to deep learning applications in the scope of robotic systems, with a focus on robotic relocalization and visual odometry estimation tasks

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Publications

2021

Deep learning point cloud odometry: Existing approaches and open challenges

Authors
Teixeira, B; Silva, H;

Publication
U.Porto Journal of Engineering

Abstract
Achieving persistent and reliable autonomy for mobile robots in challenging field mission scenarios is a long-time quest for the Robotics research community. Deep learning-based LIDAR odometry is attracting increasing research interest as a technological solution for the robot navigation problem and showing great potential for the task. In this work, an examination of the benefits of leveraging learning-based encoding representations of real-world data is provided. In addition, a broad perspective of emergent Deep Learning robust techniques to track motion and estimate scene structure for real-world applications is the focus of a deeper analysis and comprehensive comparison. Furthermore, existing Deep Learning approaches and techniques for point cloud odometry tasks are explored, and the main technological solutions are compared and discussed. Open challenges are also laid out for the reader, hopefully offering guidance to future researchers in their quest to apply deep learning to complex 3D non-matrix data to tackle localization and robot navigation problems.

2020

Deep Learning for Underwater Visual Odometry Estimation

Authors
Teixeira, B; Silva, H; Matos, A; Silva, E;

Publication
IEEE Access

Abstract

2019

Deep Learning Approaches Assessment for Underwater Scene Understanding and Egomotion Estimation

Authors
Teixeira, B; Silva, H; Matos, A; Silva, E;

Publication
OCEANS 2019 MTS/IEEE SEATTLE

Abstract