  Sensor setups of robotic platforms commonly include both camera and LiDAR as
they provide complementary information. However, fusing these two modalities
typically requires a highly accurate calibration between them. In this paper,
we propose MDPCalib which is a novel method for camera-LiDAR calibration that
requires neither human supervision nor any specific target objects. Instead, we
utilize sensor motion estimates from visual and LiDAR odometry as well as deep
learning-based 2D-pixel-to-3D-point correspondences that are obtained without
in-domain retraining. We represent camera-LiDAR calibration as an optimization
problem and minimize the costs induced by constraints from sensor motion and
point correspondences. In extensive experiments, we demonstrate that our
approach yields highly accurate extrinsic calibration parameters and is robust
to random initialization. Additionally, our approach generalizes to a wide
range of sensor setups, which we demonstrate by employing it on various robotic
platforms including a self-driving perception car, a quadruped robot, and a
UAV. To make our calibration method publicly accessible, we release the code on
our project website at http://calibration.cs.uni-freiburg.de.
