Qingwen Liu

Papers

Total Citations

H-Index

About

Qingwen Liu is a researcher advancing the frontiers of multi-robot cooperative localization and indoor positioning systems. Her work primarily focuses on developing high-accuracy localization solutions for outdoor and indoor environments, addressing critical challenges in GPS-denied or low-precision scenarios. Liu's major contributions include pioneering a laser rangefinder-based baseline measurement system for outdoor multi-robot collaborative localization (MRCL), which integrates a trilateral localization algorithm combining least-squares matrix and gradient descent methods to overcome low GPS accuracy. She has also proposed a resonant beam phase-based passive localization (RBPPL) system for indoor multi-target tracking, optimized for applications in autonomous robotics, virtual reality, and the Internet of Everything. Additionally, Liu has explored cloud computing integration for MRCL, enhancing scalability and precision in logistics and production settings. Her work, while early in citation impact—with top papers garnering 4, 2, and 1 citations respectively—demonstrates innovative approaches to foundational problems in robotics and IoT. Liu's research is notable for its practical focus on real-world deployment, bridging theoretical algorithms with hardware implementations like shared cameras and laser rangefinders. Her contributions are poised to influence future developments in autonomous navigation and smart environments.