Papers
12
Total Citations
380
H-Index
8
About
Changhong Fu is a prominent researcher whose work spans unmanned aerial vehicle (UAV) autonomy, visual object tracking, fuzzy logic control, and robotic perception. His research has made significant strides in addressing real-world challenges facing aerial robotics, particularly in demanding conditions such as nighttime environments and uncertain sensor inputs. His 2022 paper on unsupervised domain adaptation for nighttime aerial tracking (111 citations) pioneered a framework that dramatically extended the operational envelope of aerial robots beyond favorable lighting conditions. Complementing this, his work on multi-regularized correlation filters (84 citations) advanced discriminative tracking methods for UAV platforms, improving robustness and accuracy in complex scenes. Fu has also made notable contributions to UAV control systems, developing input uncertainty-sensitive nonsingleton fuzzy logic controllers (65 citations) that enhance long-term quadrotor navigation reliability. His earlier research on autonomous shipboard UAV landing demonstrated a practical vision-based approach to a highly challenging problem. More recently, Fu has extended his focus toward semantic scene understanding and active mapping for robotic inspection tasks. With a career bridging foundational control theory and cutting-edge deep learning, Fu's work continues to push the boundaries of intelligent aerial autonomy.
Research Focus
Key Achievements
Top Papers
- 1Unsupervised Domain Adaptation for Nighttime Aerial Tracking111 citations · 2022
- 2Multi-Regularized Correlation Filter for UAV Tracking and Self-Localization84 citations · 2021
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- 4Toward visual autonomous ship board landing of a VTOL UAV38 citations · 2013
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- 6Local Perception-Aware Transformer for Aerial Tracking15 citations · 2022
- 7Semantics-Aware Receding Horizon Planner for Object-Centric Active Mapping12 citations · 2024
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