| Citation: |
MAO Qinghua，CHAI Jianquan，CHEN Yanzhang，et al. A three-dimensional reconstruction method of coal mine tunnel fused with LiDAR and IMU[J]. Coal Science and Technology，2025，53（2）：351−362. DOI: 10.12438/cst.2024-1386
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In view of the problems of typical unstructured, degraded characteristics and large-scale environment in coal mine tunnel, and the 3D reconstruction of coal mine tunnel is prone to low pose estimation accuracy and large cumulative drift error, a 3D reconstruction method of coal mine tunnel fused with lidar and inertial measurement unit (IMU) is proposed. In this method, the residual function of the lidar observation model and the prior state deviation of the IMU prediction model are tightly coupled through iterative kalman filter, and a more accurate posteriori state is obtained through state update, which provides reliable pose estimation for the degraded environment. In order to reduce the cumulative drift error in the process of tunnel 3D model reconstruction, a loopback detection algorithm based on Voxelized Generalized ICP (VGICP) is proposed. It is registered in a voxel-based single to multi-distribution mode, so as to complete the selection and accurate matching of loopback keyframes, realize the global pose correction of loopback keyframes, and effectively reduce the cumulative drift error of 3D reconstruction of coal mine tunnel. Compared with the A-LOAM and LEGO-LOAM algorithms, the proposed algorithm has significantly improved the accuracy and global consistency of pose estimation. Experimental results on public datasets show that the root mean square errors of RPE and APE of the proposed algorithm are
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