PseudoCal: Towards Initialisation-Free Deep Learning-Based Camera-LiDAR
  Self-Calibration
Mathieu Cocheteux Julien Moreau Franck Davoine
Camera-LiDAR extrinsic calibration is a critical task for multi-sensor fusion
in autonomous systems, such as self-driving vehicles and mobile robots.
Traditional techniques often require manual intervention or specific
environments, making them labour-intensive and error-prone. Existing deep
learning-based self-calibration methods focus on small realignments and still
rely on initial estimates, limiting their practicality. In this paper, we
present PseudoCal, a novel self-calibration method that overcomes these
limitations by leveraging the pseudo-LiDAR concept and working directly in the
3D space instead of limiting itself to the camera field of view. In typical
autonomous vehicle and robotics contexts and conventions, PseudoCal is able to
perform one-shot calibration quasi-independently of initial parameter
estimates, addressing extreme cases that remain unsolved by existing
approaches.