Underwater Environment
Underwater environment research focuses on developing robust and efficient methods for exploration, mapping, and object manipulation in challenging aquatic settings. Current efforts concentrate on improving autonomous underwater vehicle (AUV) navigation and perception using advanced computer vision techniques (e.g., convolutional neural networks, transformers, and deep unfolding networks), acoustic sensing, and innovative robotic designs (e.g., biomimetic robots, soft robots). These advancements are crucial for various applications, including search and rescue, environmental monitoring, and infrastructure inspection, ultimately enhancing our understanding and interaction with the underwater world.
Papers
Iterative Encoding-Decoding VAEs Anomaly Detection in NOAA's DART Time Series: A Machine Learning Approach for Enhancing Data Integrity for NASA's GRACE-FO Verification and Validation
Kevin Lee
NeuroPump: Simultaneous Geometric and Color Rectification for Underwater Images
Yue Guo, Haoxiang Liao, Haibin Ling, Bingyao Huang