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

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

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...

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Bibliographic Details
Main Authors: Han Hu, Ying Jiang, Zeyuan Dai, Rui Hao, Wenna Fan, Lihua Zhang, Xuming Ge, Bo Xu, Qing Zhu
Format: Article
Language:English
Published: Elsevier 2025-05-01
Series:International Journal of Applied Earth Observations and Geoinformation
Subjects:
Tunnel mapping system
LiDAR-camera calibration
Online calibration
Cross-modality matching
Irregular vibrations
Surface parameterization
Online Access:http://www.sciencedirect.com/science/article/pii/S1569843225002031
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Summary: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.
ISSN:1569-8432

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