Jiangbo Song
Papers
2
Total Citations
21
H-Index
2
About
Jiangbo Song is a leading researcher in autonomous navigation and multi-sensor fusion, with a primary focus on underwater and indoor robotic localization. His most impactful work, "Acoustic-VINS: Tightly Coupled Acoustic-Visual-Inertial Navigation System for Autonomous Underwater Vehicles" (2023, 18 citations), addresses a critical challenge in underwater robotics: the inability of visual-inertial systems to determine global position. By tightly integrating long baseline (LBL) acoustic positioning with visual and inertial data, Song developed a robust navigation framework that enables autonomous underwater vehicles (AUVs) to operate accurately in GPS-denied environments. This contribution has significant implications for ocean exploration, underwater infrastructure inspection, and environmental monitoring. More recently, in "Cooperative Indoor Localization Using Mobile Robot Anchors via Factor Graph Optimization" (2025, 3 citations), Song extends his expertise to indoor settings, proposing a cooperative localization method that leverages mobile robot anchors and factor graph optimization to overcome the limitations of traditional indoor positioning systems. His work consistently demonstrates a talent for fusing heterogeneous sensor data to achieve reliable, real-time localization across challenging domains. Song’s research is highly relevant for students and engineers working on autonomous systems, sensor fusion, and field robotics.
Research Focus
Key Achievements
Top Papers
- 1
- 2