Direct, Targetless and Automatic Joint Calibration of LiDAR-Camera Intrinsic and Extrinsic

Abstract

This paper presents a direct, targetless, and automatic LiDAR-Camera joint calibration method that effectively overcomes the intrinsic precision limitations. We propose an iterative two-stage optimization methodology that leverages 3D LiDAR measurements to simultaneously refine both intrinsic and extrinsic. In the first stage, the intrinsic is optimized using a normalized information distance (NID) metric, an information-theoretic measure that quantifies the statistical alignment between LiDAR and image intensities, while initial extrinsic parameters derived from CAD specifications facilitate the projection of LiDAR point clouds onto the camera image plane. In the second stage, the refined intrinsic guides further optimization of extrinsic using the same NID-based evaluation metrics. This alternating process iteratively enhances both intrinsic and extrinsic through their mutual interdependence. Experiments across multiple datasets demonstrate that our method achieves sub-pixel intrinsic accuracy and extrinsic parameters that closely align with CAD specifications, validating the superior performance of our methodology for sensor fusion applications.