CalibMutiL: Online Calibration Of LiDAR-Camera Based On Multi-level Visual Feature Fusion

Abstract

Multi-sensor fusion is a key technology in the field of autonomous driving and robotics. Traditional offline multi-sensor fusion calibration methods rely on manual operations and fail to meet real-time requirements, while recent online calibration technologies have limited generalization capabilities. This paper proposes CalibMutiL, an end-to-end calibration network that departs from conventional deep feature fusion by leveraging multi-level RGB image features to guide point cloud alignment. CalibMutiL introduces a Multi-level Fusion module (MLF) that effectively utilizes the rich visual features of the image. In addition, we regard the alignment process as a sequence prediction problem and further improve the performance through an Iterative Refinement module (IRM). Evaluation of the KITTI odometry and raw dataset demonstrates the average calibration error reaches 0.81cm and 0.09°. The generalization tests resulted in errors of 4.24cm and 0.13°, outperforming existing methods. Our implementation will be publicly available at https://github.com/VIP-G/CalibMutiL.