  Accurate LiDAR-camera extrinsic calibration is a precondition for many
multi-sensor systems in mobile robots. Most calibration methods rely on
laborious manual operations and calibration targets. While working online, the
calibration methods should be able to extract information from the environment
to construct the cross-modal data association. Convolutional neural networks
(CNNs) have powerful feature extraction ability and have been used for
calibration. However, most of the past methods solve the extrinsic as a
regression task, without considering the geometric constraints involved. In
this paper, we propose a novel end-to-end extrinsic calibration method named
DXQ-Net, using a differentiable pose estimation module for generalization. We
formulate a probabilistic model for LiDAR-camera calibration flow, yielding a
prediction of uncertainty to measure the quality of LiDAR-camera data
association. Testing experiments illustrate that our method achieves a
competitive with other methods for the translation component and
state-of-the-art performance for the rotation component. Generalization
experiments illustrate that the generalization performance of our method is
significantly better than other deep learning-based methods.
