Jianfeng Huang

Cloud Computing Center, Fuzhou University

Papers

Total Citations

H-Index

About

Jianfeng Huang is a robotics and computer vision researcher whose work sits at the intersection of deep learning, autonomous navigation, and human-robot interaction. His most recognized contributions focus on visual localization and odometry — the challenge of enabling robots and autonomous systems to accurately determine their position and orientation in complex, real-world environments. His most cited work, "Deep Global-Relative Networks for End-to-End 6-DoF Visual Localization and Odometry," addresses one of the field's persistent challenges: drift accumulation in long-term robot navigation. By fusing global and relative deep neural network architectures into a unified end-to-end framework, Huang proposed a novel approach to achieving robust six-degrees-of-freedom pose estimation, garnering over 26 citations across its iterations. His earlier research on robot collision avoidance, which combined Kinect depth sensing with global vision and human skeleton detection, demonstrates a broader commitment to improving industrial robot safety and human-robot collaboration. Taken together, Huang's body of work reflects a consistent drive to bridge theoretical deep learning advances with practical robotic systems, making him a valuable contributor to the growing field of intelligent autonomous navigation.