Yechuan Yeo
Papers
1
Total Citations
3
H-Index
1
About
Yechuan Yeo is a researcher whose work sits at the intersection of autonomous perception and environmental sensing, with a particular focus on unstructured, rural environments. His key research areas include sensor fusion, scene parsing, and road boundary estimation, where he leverages the complementary strengths of 3D LiDAR pointclouds and electro-optical (EO) imagery. In his most cited work, "Rural scene parsing and road boundary estimation by fusion of lidar pointcloud and EO images" (2016), Yeo developed a novel perception system that fuses outputs from a 3D LiDAR classifier and an image scene parser to generate a semantic 2D map of complex, off-road terrains. This map is then used to accurately estimate dirt road boundaries, a critical capability for autonomous navigation in agricultural or remote settings. While his citation count is modest, with 3 citations on this paper, the work demonstrates a practical, systems-level approach to a challenging real-world problem, highlighting his contribution to advancing perception in environments where traditional urban driving models fail. Yeo’s research is particularly valuable for students and engineers working on autonomous vehicles, field robotics, or multi-modal sensor integration in non-ideal conditions.
Research Focus
Key Achievements
Top Papers
- 1