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
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
2022
Authors
Teixeira, B; Lima, AP; Pinho, C; Viegas, D; Dias, N; Silva, H; Almeida, J;
Publication
2022 OCEANS HAMPTON ROADS
Abstract
2021
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
Authors
Teixeira, B; Silva, H; Matos, A; Silva, E;
Publication
IEEE ACCESS
Abstract
2019
Authors
Teixeira, B; Silva, H; Matos, A; Silva, E;
Publication
OCEANS 2019 MTS/IEEE SEATTLE
Abstract
The access to the final selection minute is only available to applicants.
Please check the confirmation e-mail of your application to obtain the access code.
