Syed Shabbir Ahmed

Papers

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About

Syed Shabbir Ahmed is a researcher at the forefront of multi-robot systems and autonomous navigation, with a specialized focus on indoor localization and relative state estimation. His work addresses critical challenges in enabling collaborative tasks for unmanned aerial vehicles (UAVs) operating without external infrastructure. Ahmed’s major contributions include the creation of the **MILUV dataset**, a comprehensive Multi-UAV Indoor Localization resource that integrates ultra-wideband (UWB) ranging with vision data. Spanning 217 minutes of flight time across 36 experiments with three quadcopters, this dataset provides raw timestamps and channel-impulse response data, serving as a vital benchmark for the research community. Additionally, his work on **Gaussian-Sum Filters** tackles the persistent problem of ambiguities in 3D relative pose estimation from range measurements, offering a robust solution for multi-robot coordination. While his papers are early in their citation lifecycle, their foundational nature promises significant future impact. Ahmed’s research is particularly notable for bridging practical dataset creation with advanced filtering theory, making him a rising voice in autonomous systems and collaborative robotics.