Jack Naylor

202B, Rose St Building J04

University of Sydney

Darlington, NSW, 2006

I am a PhD Candidate with the Australian Centre for Robotics at the University of Sydney developing neural scene representations for enhanced robotic perception in complex, unstructured environments. I hold a combined Bachelor of Engineering (Mechanical) Hons I/Bachelor of Science (Advanced) degree from the University of Sydney, with majors in Space Engineering and Physics and a minor in Mathematics.

My research interests lie across neural representations, computational imaging, remote sensing, embedded AI, with a particular focus to the space domain.

Previously, I was a Computer Vision Research Student with Nearmap developing targeted small object extraction and reconstruction approaches for thin linear features in aerial imagery. This was the subject for my Honours thesis.

News

May 5, 2024	I have been accepted to speak at the International Astronautical Congress (IAC) in Milan this October on a plenary session entitled: “Intelligent Space: Big Data, Advanced Algorithms, and Autonomous Robotics in Space”!
Apr 4, 2024	Honoured to be presented with two 2023 Dean’s Awards for Outstanding Tutoring and Enhancing Student Life for the Faculty of Engineering this week!
Jun 10, 2023	Fantastic meeting everyone at ICRA 2023! Looking forward to visits to the University of Siegen, DLR, EPFL and more over the coming weeks!
Jan 22, 2023	NOCaL has been accepted to ICRA 2023! Looking forward to meeting all in London at the end of May!
Mar 15, 2022	Began my PhD with the Australian Centre for Field Robotics, you can read more about my project here.

Selected Publications

NOCaL: Calibration-Free Semi-Supervised Learning of Odometry and Camera Intrinsics

Ryan Griffiths, Jack Naylor, and Donald G. Dansereau

2022

arXiv Bib Website

@article{griffiths2022nocal,
  doi = {10.48550/ARXIV.2210.07435},
  author = {Griffiths, Ryan and Naylor, Jack and Dansereau, Donald G.},
  keywords = {Robotics (cs.RO), Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {NOCaL: Calibration-Free Semi-Supervised Learning of Odometry and Camera Intrinsics},
  publisher = {arXiv},
  year = {2022},
  copyright = {arXiv.org perpetual, non-exclusive license},
}