Papers
2
Total Citations
11
H-Index
2
About
Shuhui Bu is a leading researcher in robotics perception and 3D scene understanding, with a primary focus on advancing simultaneous localization and mapping (SLAM) systems. His most impactful work, "G²-Mapping: General Gaussian Mapping for Monocular, RGB-D, and LiDAR-Inertial-Visual Systems" (2025), introduces a groundbreaking framework that unifies diverse sensor modalities—from monocular cameras to LiDAR-inertial setups—using 3D Gaussian points as a scene representation. This innovation directly addresses critical limitations in applying 3D Gaussian splatting to real-time SLAM, achieving 8 citations in its first year and signaling strong early influence. Bu also contributes to practical robotics deployment through his work on sparse stereo vision for micro air vehicles (MAVs), where he improved point-feature-based algorithms to enhance depth estimation under hardware constraints. By bridging the gap between advanced 3D representations and resource-limited platforms, Bu’s research enables more robust autonomous navigation for small robots and drones. His work is essential reading for students and engineers developing next-generation mapping systems that must operate across diverse environments and sensor configurations.
Research Focus
Key Achievements
Top Papers
- 1
- 2An improved point feature‐based sparse stereo vision3 citations · 2022