Taehun Ryu ☕️

Taehun Ryu

M.S. Student

Ulsan National Institute of Science and Technology (UNIST)

Research Profile

I’m currently pursuing an M.S. in Artificial Intelligence at UNIST and working in the 3D Vision & Robotics Lab advised by Prof. Kyungdon Joo. I received B.S. in Robotics from Hanyang University ERICA, South Korea in 2024. My research focuses on sensor calibration, visual SLAM, and event-based vision for robotics.

Education

M.S. in Artificial Intelligence

2025-03-02

Ulsan National Institute of Science and Technology (UNIST)

B.S. in Robotics

2019-03-02
2024-08-31

Hanyang University ERICA

Interests

Sensor Calibration Visual SLAM Event-based Robot Vision 3D Vision
Publications
From Corners to Fiducial Tags: Revisiting Checkerboard Calibration for Event Cameras

From Corners to Fiducial Tags: Revisiting Checkerboard Calibration for Event Cameras

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2026

We propose a checkerboard-based calibration method for event cameras with a mathematical analysis of the event generation rate.
Table Robot System for Complete Restaurant Hall Automation

Table Robot System for Complete Restaurant Hall Automation

Institute of Control, Robotics and Systems (ICROS), 2024

This paper presents a table robot system for restaurant hall automation that combines ceiling-camera perception with dynamic path planning to improve service speed and reduce reliance on human servers.

Experience

Graduate Research Intern

Ulsan National Institute of Science and Technology (UNIST)

Conducted an industry-academia project at 3D Vision & Robotics Lab in collaboration with CLOBOT on multi-camera-based SLAM system development.

Education

M.S. in Artificial Intelligence

Ulsan National Institute of Science and Technology (UNIST)

B.S. in Robotics

Hanyang University ERICA

Minor in Artificial Intelligence. Early graduation. 2 years of military service. GPA: 4.18/4.5.
Recent Projects
Stereo Visual SLAM Enhancement featured image

Stereo Visual SLAM Enhancement

Integrated SSC-based ANMS into the ORB keypoint selection stage of ORB-SLAM3 to promote spatially balanced feature selection and improve tracking stability.

Multi-Robot Localization and Tracking featured image

Multi-Robot Localization and Tracking

Developed a hybrid localization framework combining robot-internal EKF estimates (IMU, wheel encoder, and LiDAR) with ceiling-camera observations.