Mengmeng Fu
Papers
4
Total Citations
31
H-Index
3
About
Mengmeng Fu is an emerging robotics researcher whose work sits at the intersection of autonomous perception, 3D scene reconstruction, and robotic manipulation. Her most recognized contribution, "Active Implicit Object Reconstruction Using Uncertainty-Guided Next-Best-View Optimization" (2023), has garnered 19 citations and introduces a novel framework that integrates implicit neural representations with active sensing strategies, enabling mobile robots to intelligently plan sensor viewpoints while balancing reconstruction accuracy and computational efficiency. This work represents a meaningful advance in how autonomous systems perceive and model their environments in real time. Building on her expertise in autonomous exploration, Fu has also investigated whole-body motion planning for unmanned ground vehicles equipped with gimbal RGB-D cameras, addressing key limitations in outdoor 3D exploration that existing methods struggle to overcome. Her more recent work on six-degree-of-freedom grasp pose detection further demonstrates her breadth, tackling the challenge of interest point selection to improve robotic manipulation in complex scenes. Across her publications, Fu consistently addresses real-world limitations in autonomous robotics—from perception and planning to physical interaction—making her a promising contributor to the field of intelligent robotic systems. Her growing citation record reflects the relevance and timeliness of her research agenda.
Research Focus
Key Achievements
Top Papers
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