  The accurate and robust calibration result of sensors is considered as an
important building block to the follow-up research in the autonomous driving
and robotics domain. The current works involving extrinsic calibration between
3D LiDARs and monocular cameras mainly focus on target-based and target-less
methods. The target-based methods are often utilized offline because of
restrictions, such as additional target design and target placement limits. The
current target-less methods suffer from feature indeterminacy and feature
mismatching in various environments. To alleviate these limitations, we propose
a novel target-less calibration approach which is based on the 2D-3D edge point
extraction using the occlusion relationship in 3D space. Based on the extracted
2D-3D point pairs, we further propose an occlusion-guided point-matching method
that improves the calibration accuracy and reduces computation costs. To
validate the effectiveness of our approach, we evaluate the method performance
qualitatively and quantitatively on real images from the KITTI dataset. The
results demonstrate that our method outperforms the existing target-less
methods and achieves low error and high robustness that can contribute to the
practical applications relying on high-quality Camera-LiDAR calibration.
