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Accurate LiDAR-camera extrinsic calibration is a precondition for manymulti-sensor systems in mobile robots. Most calibration methods rely onlaborious manual operations and calibration targets. While working online, thecalibration methods should be able to extract information from the environmentto construct the cross-modal data association. Convolutional neural networks(CNNs) have powerful feature extraction ability and have been used forcalibration. However, most of the past methods solve the extrinsic as aregression task, without considering the geometric constraints involved. Inthis paper, we propose a novel end-to-end extrinsic calibration method namedDXQ-Net, using a differentiable pose estimation module for generalization. Weformulate a probabilistic model for LiDAR-camera calibration flow, yielding aprediction of uncertainty to measure the quality of LiDAR-camera dataassociation. Testing experiments illustrate that our method achieves acompetitive with other methods for the translation component andstate-of-the-art performance for the rotation component. Generalizationexperiments illustrate that the generalization performance of our method issignificantly better than other deep learning-based methods.

DXQ-Net: Differentiable LiDAR-Camera Extrinsic Calibration Using Quality-aware Flow