Yongfu Chen

Huazhong University of Science and Technology

Papers

Total Citations

H-Index

About

Yongfu Chen is a researcher specializing in robotics, with a primary focus on simultaneous localization and mapping (SLAM) for mobile and service robots. His key research areas include scan registration algorithms, topological mapping, and efficient navigation for autonomous systems, particularly home cleaning robots. Chen’s major contributions center on improving the accuracy and efficiency of SLAM processes. His most cited work, the "Composite clustering normal distribution transform algorithm" (2020, 7 citations), enhances scan registration—a critical step for robot mapping and navigation—by refining the normal distribution transform method to achieve higher precision. Additionally, his "TVSLAM: An Efficient Topological-Vector Based SLAM Algorithm for Home Cleaning Robots" (2017, 2 citations) introduces a novel approach that combines topological and vector representations to optimize SLAM performance for domestic robots, reducing computational overhead while maintaining robust localization. Although his citation counts are modest, Chen’s work addresses practical challenges in real-world robotics, offering scalable solutions for autonomous navigation in constrained environments. His research is particularly relevant for students and engineers developing cost-effective, reliable SLAM systems for consumer robotics, highlighting the ongoing need for efficient algorithms in resource-limited platforms.