Pubs Projects Tools Join Team About Home

NOCaL: Calibration-Free Odometry

Given a pair of input frames NOCaL can render novel synthetic views of the captured scene. As NOCaL can produce novel views for multiple cameras geometries, it allows NOCaL to use this as supervision of odometry for a range of cameras.

There are a multitude of emerging imaging technologies that could benefit robotics. However the need for bespoke models, calibration and low-level processing represents a key barrier to their adoption. In this work we present NOCaL, Neural Odometry and Calibration using Light fields, a semi-supervised learning architecture capable of interpreting cameras without calibration.

  • NOCaL automatically interprets previously unseen cameras by jointly learning to estimate novel views, odometry, and camera parameters.
  • A rendering hypernetwork is leveraged to allow us to benefit from a large pool of existing data.
  • We propose using a ray based light field rendering network for supervision of odometry, which can generates views in a camera agnostic way. This allows NOCaL to be used for multiple cameras, even ones that haven't been created yet.

This allows use of a broad range of monocular cameras without calibration. This work is a key step toward automatically interpreting more general camera geometries and emerging camera technologies.

Publications

•  R. Griffiths, J. Naylor, and D. G. Dansereau, “NOCaL: Calibration-free semi-supervised learning of odometry and camera intrinsics,” Robotics and Automation (ICRA), 2023. Preprint here.

Themes

Code and Dataset

 
The code is available on GitHub here.
The dataset used is from the LearnLFOdo project and can be requested via email here.

Citing

If you find this work useful please cite
@article{griffiths2023nocal,
  title = {{NOCaL}: Calibration-Free Semi-Supervised Learning of Odometry and Camera Intrinsics},
  author = {Ryan Griffiths and Jack Naylor and Donald G. Dansereau},
  journal = {Robotics and Automation ({ICRA})},
  year = {2023}
}