Kyungjae Ahn

About

Kyungjae Ahn is a leading researcher in autonomous systems, specializing in localization, sensor fusion, and semantic mapping for mobile robots and autonomous vehicles. His work addresses critical challenges in industrial automation and self-driving technology. His highly cited 2015 study on integrating particle filters with dead reckoning for automated guided vehicles (AGVs) established a foundational approach for efficient indoor localization, earning 8 citations and advancing factory automation. More recently, his 2024 paper on Dynamic Occupancy Grid Maps (DOGMs) with semantic information, using a deep learning-based BEVFusion method with camera and LiDAR fusion, has garnered 7 citations for its innovative solution to representing object position and velocity in dynamic environments. Ahn also led the development of VIROS, Kookmin University’s autonomous vehicle for the 2018 International Autonomous Driving Competition, where he implemented a Robot Operating System (ROS)-based control system. With a focus on practical, real-world applications—from AGVs to competition-grade autonomous driving—Ahn’s work bridges theoretical advances and tangible impact, making him a key contributor to the evolution of intelligent, autonomous systems.

Research Focus

Key Achievements

Top Papers

1. 1
   A study on integration of particle filter and dead reckoning for efficient localization of automated guided vehicles

   8 citations · 2015
2. 2
   Dynamic Occupancy Grid Map with Semantic Information Using Deep Learning-Based BEVFusion Method with Camera and LiDAR Fusion

   7 citations · 2024
3. 3
   Development of Control System for International Autonomous Driving Competition Using Robot Operating System

   2 citations · 2019

Key Collaborators

Contact & Links