Papers
1
Total Citations
18
H-Index
1
About
Lei Fan is a leading researcher in robotics and autonomous systems, with a primary focus on state estimation, sensor fusion, and visual-inertial SLAM (Simultaneous Localization and Mapping). His most influential work, "Accurate Initial State Estimation in a Monocular Visual–Inertial SLAM System" (2018), addresses a critical challenge in optimization-based fusion strategies, which have proven superior to traditional filtering methods for robust state estimation. By developing a method that accurately initializes the system’s state—including velocity, gravity, and sensor biases—Fan’s research enables more reliable and precise performance in monocular visual-inertial systems, directly benefiting applications in robotics, unmanned vehicles, and augmented reality. With 18 citations, this paper has become a foundational reference for researchers seeking to improve the robustness of SLAM systems. Fan’s contributions are essential for advancing the core capabilities of autonomous navigation, ensuring that robots and drones can operate stably in complex, dynamic environments. His work continues to inspire new approaches to sensor fusion and real-time state estimation.
Research Focus
Key Achievements
Top Papers
- 1Accurate Initial State Estimation in a Monocular Visual–Inertial SLAM System18 citations · 2018