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.

翻译：随着神经网络的发展以及自动驾驶越来越受欢迎,LiDAR和相机的校准吸引了越来越多的注意力。这个校准任务是多式的,摄影机所捕捉的丰富颜色和纹理信息以及来自LiDAR的准确三维空间信息对于下游任务来说具有令人难以置信的重要意义。当前研究兴趣主要侧重于通过信息融合获得准确校准结果。然而,它们很少分析校准结果是否正确,这在现实世界的参数中可能非常重要。例如,在大规模生产中,LiDARARs和每部智能汽车的相机必须获得很好的校准,因为汽车离开生产线,而在汽车生命期的剩余时间里,LDARs和相机的构成也应不断受到监督,以确保安全。对于这一点,本文建议通过自我检查算法来判断极限参数是否正确无误,通过引入基于从相机和LiDAROrb进一步整合的信息进行二分解的二分解网络。此外,由于从汽车离开生产线路段时,汽车就必须得到很好的校正校校校校校的校校校校校校,因此,在我们的数据库中没有不断校订我们的智能数据库数据库。