Papers
3
Total Citations
16
H-Index
2
About
Liangbo Xie is advancing the frontier of autonomous navigation through pioneering work in LiDAR-based perception and mapping for mobile robotics. His research centers on three critical challenges: precise self-localization in indoor environments, robust static mapping amidst urban chaos, and real-time dynamic object segmentation. In his 2023 work on reflective markers and joint optimization with FIM and GDOP, Xie developed a novel algorithm that dramatically improves LiDAR self-positioning accuracy—a foundational contribution to SLAM technology used in autonomous driving and UAV navigation. His 2024 study on convex hull triangle mesh-based static mapping directly addresses the persistent problem of dynamic interference from vehicles and pedestrians, enabling reliable map creation for path planning in highly dynamic environments. Most recently, his 2025 paper on FP-MOS introduces a frame-to-frame prediction framework for segmenting moving objects in LiDAR data, a vital capability for obstacle avoidance and trajectory prediction. With over 16 citations across these flagship publications, Xie’s work is shaping the next generation of robust, real-time perception systems for unmanned robotic operations.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3