  With the development of neural networks and the increasing popularity of
automatic driving, the calibration of the LiDAR and the camera has attracted
more and more attention. This calibration task is multi-modal, where the rich
color and texture information captured by the camera and the accurate
three-dimensional spatial information from the LiDAR is incredibly significant
for downstream tasks. Current research interests mainly focus on obtaining
accurate calibration results through information fusion. However, they seldom
analyze whether the calibrated results are correct or not, which could be of
significant importance in real-world applications. For example, in large-scale
production, the LiDARs and the cameras of each smart car have to get
well-calibrated as the car leaves the production line, while in the rest of the
car life period, the poses of the LiDARs and cameras should also get
continually supervised to ensure the security. To this end, this paper proposes
a self-checking algorithm to judge whether the extrinsic parameters are
well-calibrated by introducing a binary classification network based on the
fused information from the camera and the LiDAR. Moreover, since there is no
such dataset for the task in this work, we further generate a new dataset
branch from the KITTI dataset tailored for the task. Our experiments on the
proposed dataset branch demonstrate the performance of our method. To the best
of our knowledge, this is the first work to address the significance of
continually checking the calibrated extrinsic parameters for autonomous
driving. The code is open-sourced on the Github website at
https://github.com/OpenCalib/LiDAR2camera_self-check.

Computer Vision and Pattern Recognition;Artificial Intelligence