Papers
1
Total Citations
3
H-Index
1
About
Xunjie Li is a researcher focused on advancing geotechnical and mining engineering through innovative computer vision and sensing technologies. Their primary research areas include tunnel deformation detection, 3D point cloud processing, and visual simultaneous localization and mapping (VSLAM) applications in underground environments. Li’s major contribution lies in developing a novel method for mine tunnel deformation detection that integrates VSLAM with 3D dense point cloud slicing, addressing critical limitations of traditional laser scanning—namely poor flexibility, slow detection speed, and low automation in multi-site measurements. This work, published in 2023, has already garnered 3 citations, signaling its relevance to the field. By enabling non-contact, automated, and more efficient monitoring of tunnel stability, Li’s research directly supports safer and more cost-effective mining operations. Their approach represents a significant step forward in real-time structural health monitoring, offering a practical alternative to cumbersome laser-based systems. For students and researchers in mining engineering, geotechnical monitoring, or computer vision, Li’s work exemplifies how cutting-edge sensing and mapping techniques can solve real-world industrial challenges, paving the way for smarter, safer underground infrastructure management.
Research Focus
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Top Papers
- 1