Academic Journal of Engineering and Technology Science, 2024, 7(3); doi: 10.25236/AJETS.2024.070305.
Jingxuan Yan1, Kejun Huang2, Xiaoquan Huo3
1Xi’an Tieyi High School, Xi’an, Shaanxi, 710054, China
2Shaanxi Engineering Research Center for Intelligent Coal Mine, Xi’an, Shaanxi, 710054, China
3Shaanxi Coal Tongchuan Mining Co. Ltd., Tongchuan, Shaanxi, 727000, China
Efficient repair of structural damage in coal bunkers is crucial for minimizing economic losses in mining operations. Current repair practices often face challenges like poor visibility and high risk. This study proposes a novel solution using lidar SLAM(simultaneous localization and mapping) technology with the ICP (iterative closest point) algorithm to address these challenges, aiming to enhance safety and efficiency in coal bunker repairs. A specialized detection system is designed for coal bunker exploration robots, integrating 3D visualization software for real-time monitoring, attitude adjustment, and cross-sectional surveillance. Key hardware components include a laser radar for precise scanning and balance sensors for stability. Extensive experimental trials on coal bunkers validate the system's exceptional precision, with key performance metrics such as ATE (absolute trajectory error) and RTE (relative trajectory error) consistently below 0.01, meeting the rigorous demands of bunker inspection. The system efficiently detects critical structural anomalies like protruding reinforcement bars and partial wall ruptures, issuing timely warnings for potential hazards. These results validate the system's robustness and accuracy in identifying and characterizing coal bunker damage, providing actionable guidelines for safe, efficient, and technologically advanced bunker inspections.
ICP; Lidar SLAM; Coal Bunker Structural Damage Detection; 3D Scanning; Coal Storage Infrastructure Assessment
Jingxuan Yan, Kejun Huang, Xiaoquan Huo. Lidar SLAM-Enabled Precision Detection of Coal Bunker Structural Damage. Academic Journal of Engineering and Technology Science (2024) Vol. 7, Issue 3: 29-38. https://doi.org/10.25236/AJETS.2024.070305.
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