Online calibration of LiDAR-camera extrinsic parameters of tunnel mapping system with depth-constrained vibration compensation

Abstract

Tunnel mapping systems are essential for tunnel inspection, integrating sensors like LiDAR, cameras, and odometers to enhance data accuracy. However, calibration is challenging due to mechanical constraints and repetitive sensor installations, especially for LiDAR-Camera alignment. Existing methods struggle in tunnels with poor lighting and low texture, and they fail to address irregular vibrations from the flashing light system, causing instability. We propose a robust online calibration technique for LiDAR-Camera extrinsic parameters. By establishing a reversible mapping through surface parameterization, our approach ensures accurate cross-modality alignment. Additionally, we use depth constraints to stabilize adjacent camera stations, which are typically short-edge connections and prone to instability in photogrammetric bundle adjustment. This effectively mitigates irregular vibration effects. Validation in real-world tunnels confirms persistent vibration issues despite mechanical reinforcement. Our algorithm achieves precise point cloud and image alignment, reducing back-projection errors by over 50% and significantly improving data fusion accuracy in challenging conditions.