Shicheng Wang
Papers
2
Total Citations
10
H-Index
2
About
Shicheng Wang’s research focuses on advancing robotic perception and autonomous navigation, particularly for indoor and aerial systems. His work bridges computer vision and robotics, addressing critical challenges in scene modeling, target detection, and real-time spatial reasoning. Wang’s most cited paper, “Scene Modeling and Autonomous Navigation for Robots Based on Kinect System” (2012, 7 citations), introduces an incremental parameterized model that leverages both monocular RGB and RGB-D data from the Microsoft Kinect to solve 6-degree-of-freedom navigation. This work improves upon traditional pose estimation algorithms, enabling more robust feature-point tracking in cluttered indoor environments. His later study, “Spatial attention model based target detection for aerial robotic systems” (2019, 3 citations), extends these principles to unmanned aerial vehicles, proposing a biologically inspired attention mechanism for efficient target detection under dynamic conditions. Though his citation counts are modest, Wang’s contributions are notable for their practical integration of low-cost sensors and computational efficiency, offering scalable solutions for autonomous systems. His research holds promise for applications in search-and-rescue, industrial inspection, and service robotics, reflecting a commitment to making intelligent navigation accessible and reliable.
Research Focus
Key Achievements
Top Papers
- 1
- 2