Yanli Gao
Papers
2
Total Citations
15
H-Index
2
About
Yanli Gao is a leading researcher in robotics and autonomous navigation, specializing in indoor robot localization and sensor fusion. Her work centers on overcoming the challenges of precise positioning in GPS-denied environments through advanced filtering and smoothing techniques. Gao’s major contributions lie in the development of novel algorithms that integrate ultra-wideband (UWB) measurements with Kalman filtering and Rauch-Tung-Striebel (R-T-S) smoothing. Her most cited paper, "R-T-S Assisted Kalman Filtering for Robot Localization Using UWB Measurement" (2022, 9 citations), demonstrates a practical approach to enhancing localization accuracy. She further advanced the field with her 2021 work on "Range-Only UWB SLAM for Indoor Robot Localization," where she introduced a multi-interval extended finite impulse response (EFIR)-based R-T-S smoother. This innovative method significantly improves simultaneous localization and mapping (SLAM) performance by reducing estimation errors in range-only UWB systems. With a growing citation impact, Gao’s research is highly influential for students and engineers working on autonomous robots, offering robust solutions for real-world indoor navigation. Her achievements underscore a commitment to bridging theoretical filtering theory with practical robotic applications.
Research Focus
Key Achievements
Top Papers
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- 2