Baoshan Song
Papers
2
Total Citations
28
H-Index
2
About
Baoshan Song is a leading researcher in autonomous navigation and multi-sensor fusion, with a focus on enhancing localization reliability for intelligent vehicles and robots. His work addresses critical challenges in GNSS/INS integration, particularly under low-dynamic scenarios where conventional systems struggle. In his highly cited 2022 paper, "Using a Moving Antenna to Improve GNSS/INS Integration Performance Under Low-Dynamic Scenarios" (22 citations), Song introduced a novel approach that leverages antenna motion to boost state estimation consistency, offering a practical solution for consumer-level vehicle navigation. He further advanced the field with "Consistent Localization for Autonomous Robots With Inter-Vehicle GNSS Information Fusion" (6 citations), where he developed a hybrid GNSS filter that fuses onboard sensor data with inter-vehicle network information, enabling robust, consistent localization for multi-robot systems. These contributions have significant implications for autonomous driving and robotic swarms, where reliable positioning is paramount. Song’s work stands out for its innovative use of collaborative information fusion, bridging the gap between theoretical consistency and real-world deployment.
Research Focus
Key Achievements
Top Papers
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- 2