Kai-Tse Hsiao

National Yang Ming Chiao Tung University

Papers

Total Citations

H-Index

About

Kai-Tse Hsiao’s research focuses on assistive robotics, specifically developing intelligent perception systems for wheelchair robots to enhance care assistance and quality of life. His major contributions lie in integrating RGB-D sensors, such as the Microsoft Kinect, with Bayesian frameworks and adaptive online learning to enable robust visual SLAM (Simultaneous Localization and Mapping) and human tracking. In his most cited work, “RGB-D sensor based SLAM and human tracking with Bayesian framework for wheelchair robots” (2013, 13 citations), Hsiao introduced a novel approach using speeded-up robust feature (SURF) algorithms for real-time mapping and tracking, significantly improving robot autonomy in dynamic environments. His subsequent paper, “Adaptive online learning for human tracking” (2013, 2 citations), advanced this by employing a multiple-classifier system that cascades online learning with RGB-D appearance models, tightly coupling detection, recognition, and tracking to boost efficiency. These contributions demonstrate Hsiao’s impact in creating more responsive and reliable assistive technologies, laying groundwork for future innovations in human-robot interaction and healthcare robotics.