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About

Hongzhan Song is a researcher specializing in indoor localization, visible light positioning (VLP), and mobile robotics, with a particular focus on developing precise, cost-effective positioning solutions for real-world robotic applications. His most influential work, "High-Accuracy Robot Indoor Localization Scheme Based on Robot Operating System Using Visible Light Positioning" (2020), has garnered 120 citations and established him as a leading voice in leveraging existing lighting infrastructure for high-precision indoor navigation across industrial, domestic, and public transportation environments. Song's research portfolio reflects a systematic effort to advance the field: he has explored multirobot cooperative localization by integrating VLP with odometry data to improve accuracy and robustness in multi-robot systems, developed single-LED positioning schemes using angle sensors, and proposed universal decoding frameworks for VLP systems based on optical camera communication. These contributions collectively address critical challenges in signal decoding, scalability, and deployment practicality that have long hindered VLP adoption. With nearly 190 cumulative citations across his key publications, Song's work demonstrates meaningful impact within the robotics and optical wireless communication communities. His research is especially valuable for students and engineers seeking to build reliable, infrastructure-light indoor positioning systems for autonomous robots.