Pixel-level Extrinsic Self Calibration of High Resolution LiDAR and Camera in Targetless Environments
This addresses the calibration challenge for sensor fusion in robotics and autonomous systems, offering a targetless method that is incremental but practical for real-world applications.
The paper tackles the problem of extrinsic calibration between high-resolution LiDARs and RGB cameras without using targets like checkerboards, achieving pixel-level accuracy by aligning natural edge features, with results showing high robustness, accuracy, and consistency in various indoor and outdoor scenes.
In this letter, we present a novel method for automatic extrinsic calibration of high-resolution LiDARs and RGB cameras in targetless environments. Our approach does not require checkerboards but can achieve pixel-level accuracy by aligning natural edge features in the two sensors. On the theory level, we analyze the constraints imposed by edge features and the sensitivity of calibration accuracy with respect to edge distribution in the scene. On the implementation level, we carefully investigate the physical measuring principles of LiDARs and propose an efficient and accurate LiDAR edge extraction method based on point cloud voxel cutting and plane fitting. Due to the edges' richness in natural scenes, we have carried out experiments in many indoor and outdoor scenes. The results show that this method has high robustness, accuracy, and consistency. It can promote the research and application of the fusion between LiDAR and camera. We have open-sourced our code on GitHub to benefit the community.