Papers
3
Total Citations
26
H-Index
2
About
May Huang’s research lies at the intersection of robotics, simultaneous localization and mapping (SLAM), and geometric data processing, with a focus on enabling autonomous systems to perceive and navigate complex environments with high precision. Her most cited work introduces the Classified Feature-based Scan Matcher (CFSM), a closed-form algorithm that leverages geometric classification for high-speed and accurate laser scan matching—a critical component in robot localization and mapping. This contribution, published in 2013, has garnered 12 citations and remains a reference point for efficient scan matching. Huang further advanced SLAM robustness by combining visual CNN features with submap-based loop closure detection, addressing the sensitivity of 2D LIDAR-based mapping to environmental variations. Her 2018 paper on this hybrid approach, also with 12 citations, demonstrates how fusing LIDAR data with camera imagery improves accuracy in floor plan generation. Additionally, she explored real-time map generation using Constraint Delaunay Triangulation, showcasing her versatility in geometric computation. Though her citation counts are modest, Huang’s work is notable for its practical, algorithm-driven solutions to core SLAM challenges, making her a thoughtful contributor to the field of autonomous navigation.
Research Focus
Key Achievements
Top Papers
- 1High-speed and accurate laser scan matching using classified features12 citations · 2013
- 2Loop closure detection in SLAM by combining visual CNN features and submaps12 citations · 2018
- 3Real-Time Map Generation Using Constraint Delaunay Triangulation2 citations · 2011