Xiaojie Luo
Papers
2
Total Citations
7
H-Index
2
About
Xiaojie Luo is a rising researcher in robotics and autonomous systems, with a primary focus on visual simultaneous localization and mapping (VSLAM) for intelligent vehicles and mobile robots. Her work addresses a critical bottleneck in VSLAM: the trade-off between explicit geometric representations—which offer precise control but struggle with dynamic environments—and implicit neural approaches. In her 2024 paper "Bridging the Gap Between Explicit and Implicit Representations," Luo introduces a cross-data association framework that harmonizes these two paradigms, enabling more robust and accurate mapping in real-world conditions. This work has already garnered 4 citations, signaling its importance to the field. More recently, in 2025, Luo tackled the challenge of cloud–edge collaborative VSLAM with "VC-SLAM," where she leverages Variable-Order Chebyshev-KAN to optimize data transmission between resource-constrained robots and cloud servers. By reducing transmission load while maintaining real-time performance, this innovation enhances the scalability of autonomous robot fleets. With 3 citations in under a year, this paper is quickly gaining traction. Luo’s contributions are paving the way for more efficient, resilient, and practical VSLAM systems, making her a researcher to watch in the autonomous navigation community.
Research Focus
Key Achievements
Top Papers
- 1
- 2