Qingde Liu

Shandong University of Science and Technology

Papers

Total Citations

H-Index

About

Qingde Liu's research focuses on advancing indoor positioning systems for mobile robots, addressing critical challenges in cost, accuracy, and stability. His major contributions lie in developing Wi-Fi-based positioning methods that eliminate the need for expensive extra infrastructures like infrared sensors or cameras. In his highly cited 2020 work, "A robust mobile robot indoor positioning system based on Wi-Fi" (34 citations), Liu proposed a novel approach that significantly reduces hardware costs while maintaining robust performance. His earlier 2019 paper, "Mobile Robot Indoor Positioning System Based on K-ELM" (20 citations), introduced a kernel extreme learning machine method to overcome poor accuracy and system instability issues. These works have been instrumental in making indoor robot navigation more accessible and reliable for industrial and home automation applications. Liu's research demonstrates a clear trajectory from addressing fundamental limitations in existing systems to proposing practical, cost-effective solutions. His work continues to influence the field of mobile robotics and indoor localization, providing a foundation for future innovations in autonomous navigation.