Daolong Yang
Papers
3
Total Citations
11
H-Index
2
About
Daolong Yang’s research focuses on advancing autonomous systems through robust multi-sensor fusion, visual-inertial odometry (VIO), and precision robotics. His most-cited work addresses a critical vulnerability in UAV localization: sudden lighting variation that degrades VIO performance. By developing a learning-based IMU dead-reckoning method, Yang significantly enhances flight safety under challenging visual conditions, earning 6 citations since 2024. He further explores multi-sensor fusion pose perception for underground applications, tackling the unique challenges of GPS-denied, low-visibility environments in a 2025 paper. In the medical robotics domain, Yang contributes to robot-assisted minimally invasive surgery by designing an adaptive Kalman filter to suppress surgeons’ physiological tremors, improving surgical accuracy and patient safety. His work bridges theoretical innovation with practical deployment, demonstrating impact across aerial, subterranean, and surgical robotics. With a growing citation record and a focus on real-world robustness, Yang is establishing himself as a key contributor to resilient autonomous localization and human-robot interaction systems.
Research Focus
Key Achievements
Top Papers
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