Shaoquan Feng
Papers
2
Total Citations
11
H-Index
2
About
Shaoquan Feng is a leading researcher in the fields of robotics, autonomous navigation, and multi-sensor fusion, with a specific focus on Simultaneous Localization and Mapping (SLAM) and multi-object tracking. His work addresses critical challenges in enabling intelligent mobile carriers—such as mobile robots, drones, and self-driving cars—to operate reliably in complex, dynamic environments. Feng’s major contributions include the development of tightly-coupled sensor fusion frameworks that integrate stereo visual, inertial, and LiDAR data. His 2022 paper on "Tightly-coupled stereo visual-inertial-LiDAR SLAM based on graph optimization" (7 citations) pioneered a robust approach to overcome the limitations of single-sensor SLAM systems. More recently, his 2024 work "LIO-LOT: Tightly-Coupled Multi-Object Tracking and LiDAR-Inertial Odometry" (4 citations) breaks from the static-environment assumption of traditional LiDAR-Inertial odometry, enabling systems to leverage dynamic objects for enhanced localization and tracking. This innovation is vital for emerging applications requiring precise awareness of moving surroundings. With a growing citation impact, Feng’s research is shaping the next generation of autonomous systems, offering practical solutions for safer, more adaptive navigation in real-world scenarios.
Research Focus
Key Achievements
Top Papers
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