  This work proposes a novel motion guided method for target-less
self-calibration of a LiDAR and camera and use the re-projection of LiDAR
points onto the image reference frame for real-time depth upsampling. The
calibration parameters are estimated by optimizing an objective function that
penalizes distances between 2D and re-projected 3D motion vectors obtained from
time-synchronized image and point cloud sequences. For upsampling, a simple,
yet effective and time efficient formulation that minimizes depth gradients
subject to an equality constraint involving the LiDAR measurements is proposed.
Validation is performed on recorded real data from urban environments and
demonstrations that our two methods are effective and suitable to mobile
robotics and autonomous vehicle applications imposing real-time requirements is
shown.
