Guangyao Pan

Qingdao University of Science and Technology

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About

Guangyao Pan is a researcher focused on advancing indoor localization and autonomous navigation for mobile robots, with particular expertise in sensor fusion and adaptive algorithms. His most-cited work, "Indoor Localization Based on Fusion of AprilTag and Adaptive Monte Carlo" (2021, 9 citations), addresses a critical challenge in robotics: overcoming odometry errors that degrade localization accuracy in wheeled mobile robots. By integrating AprilTag—a visual fiducial system known for its real-time performance and high local precision—with an Adaptive Monte Carlo Localization framework, Pan developed a robust hybrid approach that significantly improves positioning reliability in indoor environments. This contribution bridges the gap between visual markers and probabilistic filtering, offering a practical solution for applications ranging from warehouse automation to service robotics. Pan’s work demonstrates a keen ability to combine theoretical rigor with real-world deployability, making his research valuable for engineers and scientists working on autonomous systems. His ongoing efforts continue to push the boundaries of how robots perceive and navigate complex indoor spaces, with potential impacts on logistics, healthcare, and smart infrastructure.