Yueqian Liu
Papers
1
Total Citations
102
H-Index
1
About
Yueqian Liu is a robotics researcher specializing in simultaneous localization and mapping (SLAM) for resource-constrained robots operating in dynamic environments. His work addresses a critical gap in autonomous navigation: while traditional SLAM algorithms perform well in static settings, they frequently fail when faced with moving objects. Liu’s major contribution lies in integrating deep learning-based semantic information into SLAM systems, enabling robots to identify and filter out dynamic elements in real time. His most-cited paper, "RGB-D Inertial Odometry for a Resource-Restricted Robot in Dynamic Environments" (2022), has garnered 102 citations, reflecting its impact on practical, low-power robotics. By combining RGB-D cameras with inertial measurement units, Liu’s approach allows small, cost-effective robots to maintain accurate localization even in cluttered, unpredictable spaces—a key enabler for applications in warehouse automation, search-and-rescue, and service robotics. His work bridges the gap between theoretical SLAM advances and real-world deployment, making autonomous navigation more robust and accessible.
Research Focus
Key Achievements
Top Papers
- 1RGB-D Inertial Odometry for a Resource-Restricted Robot in Dynamic Environments102 citations · 2022