Simultaneous localization and mapping (SLAM) is the key technology for the automation of intelligent mining equipment and the digitization of the mining environment. However, the shotcrete surface and symmetrical roadway in underground coal mines make light detection and ranging (LiDAR) SLAM prone to degeneration, which leads to the failure of mobile robot localization and mapping. To address these issues, this paper proposes a robust LiDAR SLAM method which detects and compensates for the degenerated scenes by integrating LiDAR and inertial measurement unit (IMU) data. First, the disturbance model is used to detect the direction and degree of degeneration caused by insufficient line and plane feature constraints for obtaining the factor and vector of degeneration. Second, the degenerated state is divided into rotation and translation. The pose obtained by IMU pre-integration is projected to plane features and then used for local map matching to achieve two-step degenerated compensation. Finally, a globally consistent LiDAR SLAM is implemented based on sliding window factor graph optimization. The extensive experimental results show that the proposed method achieves better robustness than LeGO-LOAM and LIO-SAM. The absolute position root mean square error (RMSE) is only 0.161 m, which provides an important reference for underground autonomous localization and navigation in intelligent mining and safety inspection.

To address the differences in spatial resolution, monitoring accuracy, and applicability across different subsidence-gradient conditions between Digital Subsidence Models (DSuMs) derived from UAV LiDAR and SBAS-InSAR, this study proposes a multi-source fusion framework that integrates systematic discrepancy correction, spatial residual modeling, and differentiated fusion for constructing a high-precision surface subsidence model. First, systematic biases between LiDAR-DSuM and InSAR-DSuM are identified through consistency analysis of subsidence differences within their overlapping areas. After systematic error correction, spatial modeling of the residuals is performed using semivariogram analysis and Kriging interpolation. The corrected residual field is then integrated with the original datasets to generate a Fused DSuM (Fusion-DSuM). Finally, the accuracies of LiDAR-DSuM, InSAR-DSuM, and Fusion-DSuM are evaluated using GNSS observations as reference data. Before fusion, LiDAR-DSuM had an Mean Absolute Error (MAE) of 143 mm and an Root Mean Square Error (RMSE) of 181 mm, whereas InSAR-DSuM had an MAE of 45 mm and an RMSE of 57 mm. Fusion-DSuM achieved an MAE of 84 mm and an RMSE of 120 mm, representing reductions of 41.26% and 33.70%, respectively, compared with the original LiDAR-DSuM. Although its accuracy was slightly lower than that of SBAS-InSAR, Fusion-DSuM complemented the limitations of SBAS-InSAR in monitoring large-gradient subsidence by incorporating high-resolution LiDAR observations. The results demonstrate that the proposed framework, which integrates systematic discrepancy correction, spatial residual modeling, and differentiated fusion of UAV LiDAR and SBAS-InSAR observations, substantially improves the accuracy of LiDAR-DSuM while providing more complete spatial characterization of surface subsidence. The proposed method supports surface crack identification, rock-mass movement inversion, and geological hazard monitoring.