Daizhuang Bai

Papers

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About

Daizhuang Bai is a researcher advancing the frontier of high-precision indoor positioning, a critical enabler for factory intelligent management and mobile robotics. His work centers on multi-sensor fusion, skillfully integrating data from Ultra Wide Band (UWB), inertial measurement units (IMU), odometers, and WiFi signal fingerprints to overcome the limitations of single-sensor systems in complex environments. Bai’s major contribution lies in developing robust fusion frameworks based on the Extended Kalman Filter (EKF), which significantly enhance positioning stability and accuracy where traditional methods falter. His most cited paper, "The IMU/UWB/odometer fusion positioning algorithm based on EKF" (2022), has garnered 9 citations, demonstrating its foundational impact. A subsequent study on multi-sensor-assisted WiFi fingerprint localization further refines these techniques, addressing poor signal stability and low accuracy. Bai’s work is notable for its practical, application-driven approach, directly tackling real-world challenges in indoor navigation. For students and researchers, his research offers a compelling model of how sensor fusion can solve complex localization problems, paving the way for smarter, more autonomous systems in industrial and robotic settings.