Papers
6
Total Citations
41
H-Index
3
About
Yaming Ou is a leading researcher in underwater robotics, specializing in perception, localization, and dense 3D reconstruction for autonomous marine systems. His work addresses the critical challenges of operating in dark, low-feature, and dynamic underwater environments where traditional vision-based methods fail. Ou’s major contributions include the development of **Water-MBSL**, a movable binocular structured light framework that achieves high-precision dense reconstruction even while the sensor is in motion—a significant advance over fixed-position systems. He also pioneered a structured light-based system for simultaneous collision-free navigation and dense mapping in unknown dark environments, enabling refined exploration where passive vision is ineffective. In localization, Ou has advanced tightly-coupled sensor fusion, integrating visual odometry with Doppler Velocity Logs (DVL) and, more recently, event cameras, inertial sensors, and acoustics to mitigate error accumulation in dead-reckoning. His work on fault diagnosis for robotic fish sensors using convolutional neural networks further enhances operational reliability. With his most-cited papers accumulating over 40 citations since 2021, Ou’s research is foundational for next-generation autonomous underwater vehicles, pushing the boundaries of what robots can perceive and achieve in the deep sea.
Research Focus
Key Achievements
Top Papers
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