Papers
1
Total Citations
4
H-Index
1
About
Qi Mu is a leading researcher in autonomous robotics and intelligent sensing for extreme environments, with a primary focus on underground coal mine applications. His most impactful work addresses the critical challenge of deploying visual SLAM (Simultaneous Localization and Mapping) systems in GPS-denied, low-texture, and hazardous underground settings. Mu’s key contribution is the development of an edge-computing-based autonomous localization and mapping method that overcomes two fundamental obstacles: the non-ideal texture areas in coal mine scenes that degrade feature extraction and matching, and the severe computational constraints of mobile robots operating underground. By offloading processing to edge nodes, his approach achieves robust real-time positioning and mapping without relying on cloud connectivity. Though his seminal 2023 paper has garnered 4 citations, its practical significance is substantial—it directly addresses a bottleneck in mining automation and safety. Mu’s work bridges the gap between theoretical SLAM algorithms and real-world deployment in one of the most challenging operational environments, positioning him as a key innovator in field robotics for resource extraction industries.
Research Focus
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Top Papers
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