From Chaos to Calibration: A Geometric Mutual Information Approach to
  Target-Free Camera LiDAR Extrinsic Calibration
Jack Borer Jeremy Tschirner Florian Ölsner Stefan Milz
Sensor fusion is vital for the safe and robust operation of autonomous
vehicles. Accurate extrinsic sensor to sensor calibration is necessary to
accurately fuse multiple sensor's data in a common spatial reference frame. In
this paper, we propose a target free extrinsic calibration algorithm that
requires no ground truth training data, artificially constrained motion
trajectories, hand engineered features or offline optimization and that is
accurate, precise and extremely robust to initialization error.
  Most current research on online camera-LiDAR extrinsic calibration requires
ground truth training data which is impossible to capture at scale. We revisit
analytical mutual information based methods first proposed in 2012 and
demonstrate that geometric features provide a robust information metric for
camera-LiDAR extrinsic calibration. We demonstrate our proposed improvement
using the KITTI and KITTI-360 fisheye data set.