Hyunga Choi

About

Hyunga Choi is a researcher advancing the frontiers of autonomous navigation and 3D spatial intelligence, with key contributions spanning simultaneous localization and mapping (SLAM), multi-robot coordination, and photorealistic indoor reconstruction. Her most cited work, a 2023 paper on a photo-realistic 3D model-based visual positioning system for large-scale indoor spaces (8 citations), demonstrates a practical breakthrough in achieving centimeter-level accuracy for real-world deployment. In foundational research, she critically analyzed reference coordinate systems in EKF-based SLAM (5 citations), clarifying how framework design impacts estimation consistency—a contribution that aids practitioners in avoiding common pitfalls. Addressing a persistent challenge in multi-robot systems, her 2025 work on hybrid artificial potential fields with wall-following (4 citations) offers a novel method to escape local minima during decentralized navigation in cluttered, nonconvex environments. Earlier, she contributed to object-oriented 3D RGB-D mapping (3 citations), laying groundwork for realistic indoor world reconstruction that bridges robotics and computer vision. With a career spanning over a decade, Choi’s work consistently tackles the gap between theoretical SLAM and robust, real-world autonomy.

Research Focus

Key Achievements

Top Papers

1. 1
   Photo-realistic 3D model based accurate visual positioning system for large-scale indoor spaces

   8 citations · 2023
2. 2
   Analysis of the reference coordinate system used in the EKF-based SLAM

   5 citations · 2014
3. 3
   Escaping Local Minima: Hybrid Artificial Potential Field with Wall-Follower for Decentralized Multi-Robot Navigation

   4 citations · 2025
4. 4
   Towards a Realistic Indoor World Reconstruction: Preliminary Results for an Object-Oriented 3D RGB-D Mapping

   3 citations · 2016

Key Collaborators

Contact & Links