Papers
21
Total Citations
663
H-Index
10
About
Qingquan Li is a prominent researcher specializing in autonomous navigation, LiDAR-based perception, and robotic systems, with particular expertise in simultaneous localization and mapping (SLAM). His work has significantly advanced the field of autonomous vehicles and mobile robotics, most notably through his highly cited comparative analysis of LiDAR SLAM-based indoor navigation (2021, 302 citations), which has become a key reference for researchers designing autonomous navigation systems. Li has made substantial contributions to 3D point cloud processing, developing innovative frameworks for transforming LiDAR data into actionable depth maps and pioneering distributed cooperative SLAM systems that enable multiple robots to collaboratively map complex environments in real time. His research extends into applied robotics, including sewer inspection using floating capsule robots, legged robot tunnel mapping, and multi-robot coordinated manipulation via reinforcement learning. Beyond robotics, Li has explored infrastructure monitoring through flexible pipeline measurements for dam deformation and smartphone-based indoor localization systems. His interdisciplinary reach even encompasses remote sensing, with contributions to ocean color atmospheric correction. With a body of work spanning foundational algorithms to real-world deployment challenges, Li's research continues to shape the trajectory of intelligent autonomous systems.
Research Focus
Key Achievements
Top Papers
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- 3RDC-SLAM: A Real-Time Distributed Cooperative SLAM System Based on 3D LiDAR53 citations · 2021
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- 6Reinforcement learning control for coordinated manipulation of multi-robots31 citations · 2015
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- 8Wi-Fi RTT/Encoder/INS-Based Robot Indoor Localization Using Smartphones24 citations · 2023
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