Xiangtao Fan
Papers
1
Total Citations
22
H-Index
1
About
Xiangtao Fan is a leading researcher in 3D computer vision, robotics perception, and spatial intelligence, with a focus on real-time simultaneous localization and mapping (SLAM) and 3D reconstruction. His most cited work, "Real-Time RGB-D SLAM Guided by Terrestrial LiDAR Point Cloud for Indoor 3-D Reconstruction and Camera Pose Estimation" (2019, 22 citations), introduces a pioneering hybrid approach that fuses low-cost RGB-D sensors—such as Microsoft Kinect—with high-precision terrestrial LiDAR data. This method dramatically improves camera pose estimation and indoor 3D reconstruction accuracy, addressing critical challenges in robotics, autonomous navigation, and augmented reality. By bridging the gap between affordable sensor streams and LiDAR-grade precision, Fan’s contributions have advanced practical SLAM systems for self-driving vehicles and robotic mapping. His research has earned recognition for enabling robust, real-time spatial awareness in complex indoor environments, making him a notable figure in the evolution of sensor fusion and autonomous perception technologies.
Research Focus
Key Achievements
Top Papers
- 1