Papers
3
Total Citations
15
H-Index
2
About
Kichun Jo is a leading researcher in autonomous vehicle localization and navigation, with a primary focus on developing robust, production-ready systems for self-driving cars. His work addresses critical challenges in long-term localization, particularly the limitations of traditional point cloud maps for mass-market automotive applications. Jo’s research on the Geodetic Normal Distribution Map (2020) introduces a novel framework that enables reliable LiDAR-based localization across diverse and changing environments, a key step toward scalable autonomy. He has also made significant contributions to hybrid route generation, integrating real-time perception with high-definition maps to create safe and efficient local paths for autonomous vehicles (2019). More recently, Jo has advanced multi-sensor fusion with a loosely-coupled localization system that incorporates bias alignment, improving accuracy and reliability in real-world driving conditions. With over 15 citations across his most influential papers, his work is foundational for engineers and researchers developing practical localization solutions. Jo’s contributions are particularly notable for bridging the gap between academic research and industrial deployment, addressing the stringent safety and reliability requirements of autonomous driving.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3