Papers
4
Total Citations
68
H-Index
2
About
Shiyu Bai is a leading researcher in multi-sensor integrated navigation and autonomous robotic localization, with a focus on high-precision, computationally efficient systems for challenging environments. Bai’s major contributions center on advancing factor graph optimization methods for fusing asynchronous absolute and relative measurements, enabling plug-and-play sensor integration that significantly improves positioning accuracy in indoor robots, UAVs, and legged platforms. Notably, Bai’s most-cited work, “A Factor Graph Optimization Method for High-Precision IMU-Based Navigation System” (2023, 38 citations), tackles the critical challenge of processing low-cost inertial measurement units through refined pre-integration techniques. This is complemented by a novel approach to asynchronous measurement fusion (2022, 27 citations), which overcomes traditional limitations in multisensor positioning. Bai’s recent innovations include DUAL-LIO, a dual-inertia aided legged odometry that leverages body constraints for lightweight, accurate localization, and a comprehensive survey on collaborative perception for clustered unmanned systems in underground spaces. With a growing citation impact and a clear trajectory toward practical, real-world deployment, Bai’s work is essential reading for researchers advancing robust navigation in GPS-denied and structurally complex environments.
Research Focus
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