Fan Xiong
Papers
1
Total Citations
5
H-Index
1
About
Fan Xiong is a robotics researcher whose work centers on advancing real-time Simultaneous Localization and Mapping (SLAM) systems, with a particular focus on loop closure detection for autonomous navigation. His key contributions lie in developing lightweight, sequence-based approaches that make SLAM more practical for embedded processors and real-world deployment. His most cited paper, "A Lightweight Sequence-based Unsupervised Loop Closure Detection" (2021), addresses a persistent challenge in robotics: creating stable, effective, and computationally efficient methods for recognizing previously visited locations. By leveraging deep learning to enhance descriptor expressiveness while maintaining a lightweight footprint, Xiong's work bridges the gap between sophisticated AI techniques and the hardware constraints of autonomous systems. This research has accumulated 5 citations, demonstrating its relevance to the SLAM community. Xiong's approach is particularly notable for its focus on unsupervised learning, reducing the need for labeled training data and making his methods more adaptable to diverse environments. His contributions are helping pave the way for more reliable, real-time navigation in autonomous robots operating in complex, real-world settings.
Research Focus
Key Achievements
Top Papers
- 1A Lightweight sequence-based Unsupervised Loop Closure Detection5 citations · 2021