Papers
2
Total Citations
7
H-Index
2
About
Bocheng Chen is a researcher specializing in robust state estimation and simultaneous localization and mapping (SLAM) for autonomous mobile robots. His work focuses on fusing data from multiple sensors—including monocular cameras, inertial measurement units (IMUs), LiDAR, and wheel encoders—to achieve accurate and resilient pose estimation in challenging, real-world environments. Chen’s key contributions address two critical weaknesses in traditional SLAM systems: scale ambiguity in monocular visual-inertial odometry and performance degradation caused by dynamic objects. His 2020 paper on tightly coupled pose estimation using vision, inertia, and wheel speed (4 citations) introduced a method that leverages wheel odometry to resolve scale issues in ground robots, significantly improving localization robustness. More recently, his 2024 work on robust LiDAR-visual-inertial odometry for dynamic scenes (3 citations) tackles the problem of moving objects corrupting feature matching and causing illumination changes, proposing a system that maintains accuracy in highly dynamic environments. Though early in his career, Chen’s research is already shaping the next generation of SLAM systems designed for real-world deployment, where static assumptions fail.
Research Focus
Key Achievements
Top Papers
- 1
- 2Robust LiDAR visual inertial odometry for dynamic scenes3 citations · 2024