Mingwei Sheng
Papers
1
Total Citations
17
H-Index
1
About
Mingwei Sheng is a robotics researcher whose work focuses on autonomous underwater vehicle (AUV) perception and navigation, with a particular emphasis on visual simultaneous localization and mapping (SLAM) in challenging underwater environments. His most cited paper, "An Underwater Image Enhancement Method for Simultaneous Localization and Mapping of Autonomous Underwater Vehicle" (2019, 17 citations), addresses a critical bottleneck in underwater robotics: the degradation of visual data due to low contrast, color distortion, and scattering in aquatic media. Sheng proposed an image enhancement technique specifically designed to improve the robustness of visual SLAM systems, enabling AUVs to achieve more accurate positioning and mapping in turbid or low-visibility conditions. This contribution is vital for advancing autonomous exploration, inspection, and monitoring tasks in marine environments. By bridging the gap between image processing and robotic localization, Sheng’s work helps make underwater robots more reliable in real-world deployments. His research sits at the intersection of computer vision, marine engineering, and autonomous systems, offering practical solutions for one of the most difficult sensing domains in robotics.
Research Focus
Key Achievements
Top Papers
- 1