Yoonkyu Yoo

About

Yoonkyu Yoo is a robotics researcher whose work centers on human-robot interaction, mobile robot navigation, and machine learning-based perception systems. His most significant contributions lie in developing intelligent methods for detecting and tracking human legs using single Laser Range Finders (LRF), enabling mobile robots to follow people naturally in real-world environments. This capability is foundational for a wide range of socially valuable applications, including human-following shopping carts, airport porter robots, and museum guide systems. Yoo's most influential work, "The Detection and Following of Human Legs Through Inductive Approaches for a Mobile Robot With a Single Laser Range Finder" (2011), has garnered 131 citations, establishing him as a notable contributor to human-friendly robot navigation. His earlier paper on leg detection and tracking (2010) further laid groundwork in this domain, while his application of Support Vector Data Description (SVDD) techniques to leg detection demonstrates his interest in bridging classical machine learning with practical robotic systems. Overall, Yoo's research addresses a critical challenge in service robotics — enabling machines to perceive and respond to human presence — making robots more intuitive, safe, and useful companions in everyday environments.

Research Focus

Key Achievements

Top Papers

1. 1
   The Detection and Following of Human Legs Through Inductive Approaches for a Mobile Robot With a Single Laser Range Finder

   131 citations · 2011
2. 2
   Detection and tracking of human legs for a mobile service robot

   30 citations · 2010
3. 3
   Detection and following of human legs using the SVDD (Support Vector Data Description) scheme for a mobile robot with a single Laser Range Finder

   10 citations · 2011

Key Collaborators

Contact & Links