High-precision 3D point cloud modeling method for coal mine roadways based on known point constraints

In response to the complex conditions such as poor lighting, weak texture, and high concentrtaions of dust in underground coal mines, the existing 3D modeling methods for coal mine roadways have the disadvantages of high costs, poor timeliness, and low accuracy. A high-precision 3D point cloud modeling method for coal mine roadways based on known point constraints was proposed. The LiDAR point cloud data was downsampled by voxel filter, followed by the use of iterative closest point (ICP) matching for the downsampled LiDAR point cloud data to extract local point cloud maps. The point cloud data was then distortion-corrected using inertial measurement unit (IMU) data. ICP was utilized to align the local point cloud maps with the distortion-corrected point cloud maps, improving the accuracy and efficiency of front-end registration. Loopback detection was incorporated in the back-end to enhance the accuracy of coal mine roadway localization and mapping. The coordinates of the known points of the coal mine roadways were obtained through control measurements using connecting traverse, providing global constraints for point cloud modeling. A combined adjustment calculation was performed on the known points and the station points determined by LiDAR simultaneous localization and mapping (SLAM). The station point coordinates were corrected, and a nonlinear optimization method was further employed to adjust the global point cloud map coordinates, thereby improving the accuracy of 3D point cloud modeling. Experimental results demonstrated that the 3D point cloud map of coal mine roadways constructed by this method had high global consistency and geometric structure authenticity, achieving high localization and mapping accuracy in underground coal mines.