Adaptive High-Resolution Dynamic Scanning System and Method for Deformation Monitoring of Underground Infrastructure

To address critical challenges in underground infrastructure safety, this paper proposes an adaptive dynamic high-resolution scanning method employing a station-based device for deformation monitoring in coal mine sealing walls. By integrating LiDAR, inertial measurements, and encoder data, we developed a hierarchical processing framework comprising feature extraction, state estimation, and spatiotemporal registration of 3D point clouds. A penalty function-based adaptive scanning strategy was formulated to optimize point cloud density and geometric fidelity during data acquisition, which was subsequently integrated into the system. An equipment placement optimization model accounting for installation geometry and region-of-interest characteristics was established, enabling efficient and accurate capture of deformation details in sealing walls of varying dimensions. The system’s performance was evaluated using two novel metrics: point cloud acquisition temporal density (PATD) and point cloud relative area error (PRAE). An ablation study comparing scanning methods and penalty term configurations demonstrated the superiority of the proposed adaptive strategy. Calibration experiments revealed that for a 1.08 m², optimal monitoring parameters were achieved, with data completeness attained at a 1.68 m monitoring distance within 51.6 s. Field validation under simulated deformation conditions, using a 5 m² ROI, evaluated three monitoring distances. Results aligned closely with theoretical predictions, identifying 3.59 m as the optimal distance and validating the system’s capability to detect centimeter-scale displacements. This work establishes a framework for continuous infrastructure health monitoring in confined underground environments.