Papers
9
Total Citations
410
H-Index
6
About
Hongli Gao is a robotics and intelligent systems researcher whose work spans simultaneous localization and mapping (SLAM), industrial robot health monitoring, and fault detection. His most influential contribution, "YOLO-SLAM: A Semantic SLAM System Towards Dynamic Environment with Geometric Constraint" (2022), has garnered 255 citations, demonstrating significant community impact in advancing robust perception for robots operating in unpredictable, dynamic environments. A defining thread throughout Gao's career is his pioneering application of singular spectrum analysis (SSA) to rotary encoder signals for diagnosing mechanical faults and monitoring the health of industrial robots — an elegant alternative to conventional vibration-based methods. Beginning with foundational work in 2019 and refined through multiple studies accumulating over 100 combined citations, this research line has established encoder-based diagnostics as a practical, non-intrusive framework for industrial condition monitoring. His more recent investigations extend this expertise into welding trajectory control and deep learning-based obstacle detection for power transmission line inspection robots. Collectively, Gao's portfolio reflects a commitment to bridging signal processing theory with real-world robotic applications, offering tools that improve both the reliability and autonomy of modern industrial systems.
Research Focus
Key Achievements
Top Papers
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- 3Improvement of an Industrial Robotic Flaw Detection System33 citations · 2022
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- 9Obstacle Detection for Power Transmission Line Based on Deep Learning2 citations · 2019