  Automated driving systems use multi-modal sensor suites to ensure the
reliable, redundant and robust perception of the operating domain, for example
camera and LiDAR. An accurate extrinsic calibration is required to fuse the
camera and LiDAR data into a common spatial reference frame required by
high-level perception functions. Over the life of the vehicle the value of the
extrinsic calibration can change due physical disturbances, introducing an
error into the high-level perception functions. Therefore there is a need for
continuous online extrinsic calibration algorithms which can automatically
update the value of the camera-LiDAR calibration during the life of the vehicle
using only sensor data.
  We propose using mutual information between the camera image's depth
estimate, provided by commonly available monocular depth estimation networks,
and the LiDAR pointcloud's geometric distance as a optimization metric for
extrinsic calibration. Our method requires no calibration target, no ground
truth training data and no expensive offline optimization. We demonstrate our
algorithm's accuracy, precision, speed and self-diagnosis capability on the
KITTI-360 data set.
