Yu‐Hsiu Lin

National Taipei University of Technology

Papers

Total Citations

H-Index

About

Yu-Hsiu Lin is a researcher whose work sits at the intersection of computer vision and autonomous navigation, with a particular focus on advancing visual odometry (VO)—a critical component for enabling robots and self-driving vehicles to understand their motion from camera input alone. Lin’s most cited paper, "Learning-Based Heatmap-Guided Model for Monocular Visual Odometry" (2025), tackles the inherent limitations of traditional VO methods, which struggle in challenging conditions such as dynamic lighting or low-texture environments. By introducing a learning-based, heatmap-guided framework, Lin’s approach enhances robustness and accuracy where conventional feature-based or direct methods fall short. This contribution is especially significant for real-world deployment in autonomous systems, where reliability is paramount. While still early in its citation trajectory, this work has already garnered 3 citations, signaling growing interest from the research community. Lin’s research demonstrates a clear commitment to bridging the gap between theoretical computer vision models and practical, deployable solutions for autonomous navigation. For students and researchers exploring the future of robotics and self-driving technology, Lin’s work offers a compelling example of how deep learning can overcome the classic challenges of visual odometry.