Yaling Pan

Papers

Total Citations

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About

Yaling Pan is a robotics researcher whose work focuses on advancing human-robot interaction (HRI) and visual simultaneous localization and mapping (VSLAM). Her key contributions address two critical challenges in mobile robotics: reliable person following under partial occlusion and efficient cloud-based mapping. In her highly cited 2023 paper on robot person following (RPF), Pan tackles the common yet difficult problem of tracking individuals when they are partially hidden from view—a limitation of many existing systems that assume full observation. This work has already garnered 21 citations, reflecting its importance for real-world HRI applications like assistive robots and autonomous companions. Additionally, Pan’s research on cloud learning-based VSLAM proposes a hybrid approach that combines edge model-based methods with cloud learning, reducing the computational burden on individual robots by eliminating the need to build all submaps locally. With 6 citations, this work highlights her innovative thinking in making learning-based VSLAM more practical for mobile robots. Pan’s research is paving the way for more robust, intelligent, and computationally efficient robotic systems that can better interact with and navigate human environments.