MRCNet: Multiresolution LiDAR-Camera Calibration Using Optical Center Distance Loss Network

The integration of cameras and LiDAR sensors has emerged as a promising approach to enhance environmental perception and 3D reconstruction capabilities in autonomous vehicles and robotic systems. Precise extrinsic calibration is of paramount importance to achieve effective multi-sensor fusion applications. Traditional calibration methods often rely on manual procedures and specific calibration targets, which can be time-consuming and prone to errors. In contrast, Convolutional Neural Networks (CNNs) have shown potential in devising end-to-end calibration systems, leveraging their ability to extract robust features automatically. In this paper, MRCNet, an online end-to-end LiDAR-camera calibration network, which overcomes the limitations of traditional methods and previous CNN-based approaches, is proposed. This article introduces a multi-resolution feature extraction module, enabling the extraction of comprehensive and informative features from RGB images and depth images derived from point clouds. Additionally, the optical center distance loss, a novel concept that accounts for the camera's optical imaging characteristics, facilitating more effective feature extraction is incorporated. MRCNet is the first online calibration network that considers the influence of camera imaging properties. This paper employs an iterative refinement process to progressively estimate the calibration error, allowing online extrinsic estimation. Evaluation tests on the KITTI Odometry dataset demonstrate the superior performance of MRCNet compared to existing learning-based methods, achieving a mean absolute calibration error of 0.350cm in translation and 0.033° in rotation. Furthermore, ablation studies validate the effectiveness of the modules of MRCNet. The code for MRCNet will be made publicly available at: https://github.com/AlexWang0214/MRCNet.