Extrinsic Calibration of LiDAR and Camera via Intensity-Aware Deep Line Registration

Abstract

Accurate extrinsic calibration between the light detection and ranging (LiDAR) and camera is a critical step for sensor fusion tasks. Existing calibration methods often rely on artificial calibration targets or distinct visual textures, which may not be available in many real-world environments. In addition, conventional LiDAR systems often capture sparse point clouds, which limits feature extraction and matching in calibration tasks. In this work, we propose a novel extrinsic calibration framework that leverages intensity-aware deep line registration. Our approach first generates dense point clouds by incrementally registering consecutive LiDAR frames and voxel filtering. This dense point cloud serves as the basis for generating high-resolution intensity maps. Next, we apply deep learning-based line detection algorithms to extract robust line features from both the intensity map and the corresponding camera image. By minimising a distance-based objective function formulated with the 3D line points and 2D image lines, we estimate the extrinsic parameters through optimisation process. Experimental results show that our method achieves sub-pixel reprojection accuracy and robustness in various environments. Our calibration method is cost-effective, easy to deploy and suitable for real-time robotic applications without the need for artificial targets.