Xungao Zhong
Papers
21
Total Citations
250
H-Index
8
About
Xungao Zhong is a leading researcher in autonomous robotics, specializing in visual servoing, sensor fusion, and motion planning for mobile and manipulator systems. His work bridges the gap between robust state estimation and adaptive control, enabling robots to operate reliably in unstructured environments. Zhong’s most influential contribution is the LIO-Fusion system (2023, 31 citations), a reinforced LiDAR-inertial odometry framework that optimally fuses GNSS, relocalization, and wheel odometry for accurate 6-DoF movement estimation—a critical enabler for autonomous navigation in complex settings. He has also pioneered vision-based control, notably through a Kalman-neural-network filtering scheme (2014, 61 citations) that achieves model-independent visual servoing with global stability, and a velocity-change-space dynamic motion planner (2014, 30 citations) for collision avoidance and formation control. His recent work on pixel-reasoning grasping with deep EDINet (2022, 14 citations) advances fine manipulation of novel objects. With over 200 cumulative citations, Zhong’s research consistently integrates learning-based methods with classical filtering and planning, offering practical solutions for real-world robotics challenges. His achievements underscore a career dedicated to making autonomous systems more perceptive, adaptive, and dependable.
Research Focus
Key Achievements
Top Papers
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- 5Adaptive Neuro-Filtering Based Visual Servo Control of a Robotic Manipulator16 citations · 2019
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