  The fusion of LiDARs and cameras has been increasingly adopted in autonomous
driving for perception tasks. The performance of such fusion-based algorithms
largely depends on the accuracy of sensor calibration, which is challenging due
to the difficulty of identifying common features across different data
modalities. Previously, many calibration methods involved specific targets
and/or manual intervention, which has proven to be cumbersome and costly.
Learning-based online calibration methods have been proposed, but their
performance is barely satisfactory in most cases. These methods usually suffer
from issues such as sparse feature maps, unreliable cross-modality association,
inaccurate calibration parameter regression, etc. In this paper, to address
these issues, we propose CalibFormer, an end-to-end network for automatic
LiDAR-camera calibration. We aggregate multiple layers of camera and LiDAR
image features to achieve high-resolution representations. A multi-head
correlation module is utilized to identify correlations between features more
accurately. Lastly, we employ transformer architectures to estimate accurate
calibration parameters from the correlation information. Our method achieved a
mean translation error of $0.8751 \mathrm{cm}$ and a mean rotation error of
$0.0562 ^{\circ}$ on the KITTI dataset, surpassing existing state-of-the-art
methods and demonstrating strong robustness, accuracy, and generalization
capabilities.
