Pedro Porto Buarque de Gusmão
Pedro Gusmao
Department of Computer Science,
Robert Hooke Building
Directions Postal Address
Themes:
Biography
I currently hold a postdoctoral research position at the Cyber-Physical Systems group, working on the NIST grant “Pervasive, Accurate, and Reliable Location Based Services for Emergency Responders".
I received my bachelor’s degree in Telecommunication Engineering from the University of São Paulo and my master’s degree on the same field from the Politecnico di Torino in a double degree program. During my MSc studies, I was a recipient of an Alta Scuola Politecnica scholarship; and in 2017, I obtained my PhD from the same Politecnico.
My research interests include computer vision, machine learning and signal processing.
Selected Publications
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DeepTIO: A Deep Thermal−Inertial Odometry with Visual Hallucination
M.R.U. Saputra P.P.B. de Gusmao C.X. Lu Y. Almalioglu S. Rosa C. Chen J. Wahlstrom W. Wang A. Markham and N. Trigoni
In IEEE Robotics and Automation Letters (RAL) + IEEE ICRA 2020. 2020.
Details about DeepTIO: A Deep Thermal−Inertial Odometry with Visual Hallucination | BibTeX data for DeepTIO: A Deep Thermal−Inertial Odometry with Visual Hallucination | Download (pdf) of DeepTIO: A Deep Thermal−Inertial Odometry with Visual Hallucination
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Sensor Fusion for Magneto−Inductive Navigation
Johan Wahlström Manon Kok Pedro Porto Buarque de Gusmsao Traian E. Abrudan Niki Trigoni and Andrew Markham
In IEEE Sensors Journal. 2019.
Details about Sensor Fusion for Magneto−Inductive Navigation | BibTeX data for Sensor Fusion for Magneto−Inductive Navigation | DOI (10.1109/JSEN.2019.2942451) | Link to Sensor Fusion for Magneto−Inductive Navigation
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Distilling Knowledge From a Deep Pose Regressor Network
Muhamad Risqi U. Saputra Pedro P. B. de Gusmao Yasin Almalioglu Andrew Markham and Niki Trigoni
In IEEE/CVF International Conference on Computer Vision (ICCV). 2019.
Details about Distilling Knowledge From a Deep Pose Regressor Network | BibTeX data for Distilling Knowledge From a Deep Pose Regressor Network | Download (pdf) of Distilling Knowledge From a Deep Pose Regressor Network