  In this paper, we propose a novel online self-calibration approach for Light
Detection and Ranging (LiDAR) and camera sensors. Compared to the previous
CNN-based methods that concatenate the feature maps of the RGB image and
decalibrated depth image, we exploit the cost volume inspired by the PWC-Net
for feature matching. Besides the smooth L1-Loss of the predicted extrinsic
calibration parameters, an additional point cloud loss is applied. Instead of
regress the extrinsic parameters between LiDAR and camera directly, we predict
the decalibrated deviation from initial calibration to the ground truth. During
inference, the calibration error decreases further with the usage of iterative
refinement and the temporal filtering approach. The evaluation results on the
KITTI dataset illustrate that our approach outperforms CNN-based
state-of-the-art methods in terms of a mean absolute calibration error of
0.297cm in translation and 0.017{\deg} in rotation with miscalibration
magnitudes of up to 1.5m and 20{\deg}.
