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About

Kejie Qiu is a researcher specializing in robot perception, sensor fusion, and spatial localization, with contributions spanning visual-inertial odometry, multi-sensor calibration, and indoor positioning systems. His work addresses fundamental challenges in enabling autonomous robots and augmented reality systems to accurately understand and navigate their environments. Qiu's most impactful contributions include pioneering monocular visual-inertial approaches for tracking dynamic 3D objects in six degrees of freedom — eliminating the need for expensive depth sensors — and developing robust temporal and rotational calibration methods for heterogeneous sensor systems, each accumulating 65 citations. His earlier research explored cost-effective indoor localization using Visible Light Communication (VLC), leveraging everyday LED lighting infrastructure to achieve precise positioning for mobile robots and consumer devices, work that attracted sustained interest across multiple publications. His research portfolio also demonstrates a consistent focus on global localization using semantic edge alignment and compact 3D map representations, addressing the critical challenge of drift-free positioning in GPS-denied environments. More recently, his work on RenderNet introduced virtual viewpoint synthesis to tackle large-scale indoor visual relocalization. Collectively, Qiu's research reflects a sophisticated trajectory from low-cost sensing solutions to advanced vision-based state estimation, making meaningful contributions to the fields of robotics, computer vision, and augmented reality.