Passionate student specializing in robot perception, with a talent for developing robotic system software. Dedicated to advancing research in robust perception techniques to enhance the reliability of outdoor mobile robots.
Recent graduate from Kyung Hee University with a dual major in Electronic Engineering and Software Convergence, Robot Vision Track(August 2024). Gained industry experience through internships at NAVER LABS and ROBROS.
M.S. in Robotics, Upcoming student starts from 2025
KAIST
B.S. in Electronic Engineering, Software Convergence (Robot Vision Track), 2019 - 2024
Kyung Hee University
This research introduces an approach to enhancing autonomous mobile robot navigation in diverse outdoor environments. We developed a method that transforms 3D LiDAR point cloud data into grayscale heightmaps, enabling more accurate assessment of ground textures. Our system classifies terrains in both static and dynamic environments, incorporating IMU data to account for robot motion influenced by terrain. The method demonstrates superior performance in texture analysis compared to direct point cloud analysis techniques, significantly improving the ability of mobile robots to navigate safely and efficiently across various outdoor terrains. This advancement is crucial for protecting sensitive equipment and cargo while expanding the operational capabilities of autonomous robots in complex, real-world settings.
ROS1 / ROS2 / RKNN / gRPC / MQTT / CMake / Flutter
ROS 1 / MuJoCo / PyTorch
Mobile Robot / ROS / ORB-SLAM2