Alex Junho Lee

About

Alex Junho Lee is a robotics and autonomous systems researcher whose work spans simultaneous localization and mapping (SLAM), sensor fusion, and robot perception. He is perhaps best known for **DynaVINS** (2022), a visual-inertial SLAM framework designed specifically for dynamic real-world environments — a significant advance over conventional SLAM algorithms that assume static landmarks. This work has garnered over 123 citations, reflecting its immediate relevance to service robots, drones, and autonomous vehicles. Lee's **ViViD++ dataset** (2022, 77 citations) further demonstrates his commitment to robust perception, providing a rich benchmark for vision systems operating across challenging and varying luminance conditions. His survey on robotics technologies for civil infrastructure inspection (2022, 53 citations) highlights a broader applied dimension to his research, connecting autonomous navigation with real-world societal needs. More recently, Lee has pushed into cross-modal place recognition through LiDAR-camera loop constraints and explored language-guided planning via Monte-Carlo Tree Search, signaling an expanding interest in semantic reasoning for robotics. Collectively, his contributions establish him as a versatile and impactful voice in modern autonomous systems research.

Research Focus

Key Achievements

Top Papers

1. 1
   <i>DynaVINS:</i> A Visual-Inertial SLAM for Dynamic Environments

   123 citations · 2022
2. 2
   ViViD++ : Vision for Visibility Dataset

   77 citations · 2022
3. 3
   Survey of robotics technologies for civil infrastructure inspection

   53 citations · 2022
4. 4
   (LC): LiDAR-Camera Loop Constraints for Cross-Modal Place Recognition

   23 citations · 2023
5. 5
   DynaVINS: A Visual-Inertial SLAM for Dynamic Environments

   9 citations · 2022
6. 6
   LGMCTS: Language-Guided Monte-Carlo Tree Search for Executable Semantic Object Rearrangement

   3 citations · 2023

Key Collaborators

Contact & Links