Shan Su
Papers
1
Total Citations
6
H-Index
1
About
Shan Su is a robotics researcher whose work lies at the intersection of simultaneous localization and mapping (SLAM) and perception for dynamic environments. Their most cited paper, "A Novel Lidar Inertial Odometry with Moving Object Detection for Dynamic Scenes" (2022), addresses a critical limitation in traditional SLAM algorithms: their inability to handle moving objects. By integrating lidar inertial odometry with real-time moving object detection, Su’s approach enables robots to maintain robust localization and mapping even in highly dynamic scenes—a key challenge for autonomous navigation in crowded or unpredictable spaces. This contribution has garnered 6 citations, reflecting its relevance to researchers tackling real-world deployment of mobile robots. Su’s work is particularly notable for bridging the gap between theoretical SLAM frameworks and practical, safety-critical applications, such as autonomous driving or warehouse robotics. Their research continues to push the boundaries of how robots perceive and interact with changing environments, making them a rising voice in the field of intelligent robotics and perception systems.
Research Focus
Key Achievements
Top Papers
- 1