Shengquan Li
Papers
2
Total Citations
26
H-Index
2
About
Shengquan Li is a leading researcher in underwater robotics and computer vision, with a focus on advancing autonomous perception and inspection systems for challenging subsea environments. His work centers on two critical challenges: precise 6D pose estimation for multi-robot localization and automated damage detection for large-scale underwater infrastructure. In his highly cited 2023 paper, "ROV6D: 6D Pose Estimation Benchmark Dataset for Underwater Remotely Operated Vehicles" (18 citations), Li introduced a specialized benchmark dataset that addresses the fundamental need for accurate 3D spatial localization between robots—enabling critical applications like tracking, convoying, and subsea intervention. Building on this, his 2024 work, "YOLOX-DG robotic detection systems for large-scale underwater concrete structures" (8 citations), developed an AI-driven robotic inspection framework that replaces inefficient manual identification of structural damage. By integrating deep learning with robotic platforms, Li’s contributions are transforming how underwater concrete structures are monitored, significantly improving both accuracy and efficiency. His research directly supports safer, more autonomous operations in marine infrastructure maintenance, positioning him as an innovator at the intersection of robotics, computer vision, and subsea engineering.
Research Focus
Key Achievements
Top Papers
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