Abstract: Traditional measurement methods in underground mining tunnels have faced inefficiencies, limited accuracy, and operational challenges, consuming significant time and labor in complex environments. These limitations severely restrict the efficiency and quality of mine management and engineering design. To enhance the efficiency and accuracy of 3D modeling in underground tunnels, this study combines portable 3D LiDAR scanning technology with simultaneous localization and mapping. This integration enables autonomous positioning and efficient modeling without external positioning signals. The proposed approach effectively acquires high-resolution 3D data in complex environments, ensuring data accuracy and model reliability. High-resolution scanning of multiple critical areas was conducted on-site, with inertial navigation systems correcting the device's pose information. Automated data processing software was used for filtering, denoising, and modeling the collected data, leading to precise 3D tunnel models. Validation results indicate that portable laser scanning technology offers significant advantages in efficiency, accuracy, and safety, meeting the geological surveying and engineering needs of mining operations. The application of portable 3D laser scanning technology demonstrates considerable benefits in the rapid modeling of underground tunnels, providing effective technical support to improve mine management efficiency and safety. It also reveals broad application prospects.

Zhao Huijun, Liu Chao, Qi Yunpu, Song Zhanglun and Xia Xu, “A Rapid Drift Modeling Method Based on Portable LiDAR Scanner” International Journal of Advanced Computer Science and Applications(IJACSA), 16(2), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160231

@article{Huijun2025,
title = {A Rapid Drift Modeling Method Based on Portable LiDAR Scanner},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160231},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160231},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {2},
author = {Zhao Huijun and Liu Chao and Qi Yunpu and Song Zhanglun and Xia Xu}
}