Papers
1
Total Citations
1
H-Index
1
About
Ye Ren is a researcher specializing in autonomous navigation and perception systems, with a particular focus on LiDAR-based environmental understanding for complex indoor and underground settings. Her major contributions center on developing robust ground segmentation algorithms that overcome the limitations of traditional methods in challenging, dynamic environments like subway stations. Her most notable work, "An Enhanced GC-RANSAC Approach for Subway Indoor Ground Segmentation" (2024), introduces an innovative hybrid approach that first uses RANSAC to extract multiple candidate planes, then applies a slope threshold and graph-cut optimization to refine segmentation. This method significantly improves accuracy and reliability in scenes with uneven terrain, clutter, and varying lighting conditions. While her citation count is currently modest at 1, her work addresses a critical gap in autonomous navigation for public transit infrastructure, demonstrating strong potential for real-world deployment in robotics and intelligent transportation systems.
Research Focus
Key Achievements
Top Papers
- 1An Enhanced GC-RANSAC Approach for Subway Indoor Ground Segmentation1 citations · 2024