Fengyang Jiang
Papers
4
Total Citations
9
H-Index
2
About
Fengyang Jiang is pioneering the integration of Large Language Models (LLMs) with 3D Scene Graphs (3DSGs) to revolutionize intelligent robotic navigation. His research focuses on spatial perception, multi-sensor fusion SLAM, and robust localization for complex indoor environments. Jiang’s major contributions include developing novel systems that harness LLMs to construct hierarchical 3DSGs, enabling robots to achieve a holistic understanding of spatial environments for more intelligent navigation. He also proposed an engineering solution for multi-sensor fusion SLAM that enhances robustness and accuracy across both indoor and outdoor scenes, featuring a scheme-switching mechanism for adaptability. His work on visual-assisted relocalization addresses the challenge of positioning in repetitive indoor scenarios like server rooms, where traditional 2D LiDAR sensors struggle. With over 9 citations across his most-cited papers published between 2023 and 2024, Jiang’s research is gaining traction for its practical impact on autonomous robotics. His notable achievement includes demonstrating how key features of 3DSGs affect LLM interpretation, paving the way for more context-aware robot navigation systems.
Research Focus
Key Achievements
Top Papers
- 1Robot Navigation Based on 3D Scene Graphs with the LLM Tooling*3 citations · 2024
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