  As an essential procedure of data fusion, LiDAR-camera calibration is
critical for autonomous vehicles and robot navigation. Most calibration methods
rely on hand-crafted features and require significant amounts of extracted
features or specific calibration targets. With the development of deep learning
(DL) techniques, some attempts take advantage of convolutional neural networks
(CNNs) to regress the 6 degrees of freedom (DOF) extrinsic parameters.
Nevertheless, the performance of these DL-based methods is reported to be worse
than the non-DL methods. This paper proposed an online LiDAR-camera extrinsic
calibration algorithm that combines the DL and the geometry methods. We define
a two-channel image named calibration flow to illustrate the deviation from the
initial projection to the ground truth. EPnP algorithm within the RANdom SAmple
Consensus (RANSAC) scheme is applied to estimate the extrinsic parameters with
2D-3D correspondences constructed by the calibration flow. Experiments on KITTI
datasets demonstrate that our proposed method is superior to the
state-of-the-art methods. Furthermore, we propose a semantic initialization
algorithm with the introduction of instance centroids (ICs). The code will be
publicly available at https://github.com/LvXudong-HIT/CFNet.
