Yiwen Si
Papers
1
Total Citations
12
H-Index
1
About
Yiwen Si is a researcher at the forefront of autonomous navigation and 3D mapping, with a focus on enabling self-driving vehicles and robots to operate reliably in complex, unstructured environments. Their most-cited work, "Fully Automatic Large-Scale Point Cloud Mapping for Low-Speed Self-Driving Vehicles in Unstructured Environments" (2021, 12 citations), introduces a robust, fully automatic mapping system that fuses data from IMU, RTK, wheel speed encoders, and LiDAR point clouds. This contribution addresses a critical challenge in field robotics: generating accurate, large-scale maps without manual intervention, even in GPS-denied or irregular terrains. By integrating multiple sensor modalities, Si’s system enhances localization and mapping reliability, directly impacting applications in autonomous mining, agriculture, and last-mile delivery. Their work demonstrates a practical pathway toward deploying low-speed autonomous vehicles in real-world settings where traditional mapping approaches fail. With a growing citation footprint, Yiwen Si is establishing a reputation for advancing sensor fusion and point cloud processing, making their research essential reading for engineers and researchers tackling autonomous navigation in unstructured environments.
Research Focus
Key Achievements
Top Papers
- 1