Mohammed Shalaby
Papers
7
Total Citations
117
H-Index
4
About
Mohammed Shalaby is a leading researcher in multi-robot systems, specializing in state estimation, relative localization, and formation control, particularly for GPS-denied environments. His work centers on enabling robotic teams—from ground agents to aerial swarms—to accurately determine each other’s positions and orientations using low-cost, inter-robot range measurements, primarily from Ultra-Wideband (UWB) radio technology. Shalaby’s major contributions include developing novel algorithms for relative pose estimation from attitude-coupled range data (60 citations), optimizing multi-robot formations to maximize estimation accuracy (17 citations), and pioneering calibration methods that characterize and correct systematic biases in UWB two-way-ranging measurements (17 citations). He has also advanced scalable decentralized state estimation through pseudomeasurements and preintegration techniques, and introduced the MILUV dataset—a comprehensive multi-UAV indoor localization benchmark with 217 minutes of flight data. His open-source Python package, *navlie*, enables rapid prototyping of state estimation algorithms on Lie groups, a critical tool for modern robotics navigation systems. With a growing citation impact and contributions spanning theory, calibration, datasets, and software, Shalaby is shaping the future of collaborative autonomous systems.
Research Focus
Key Achievements
Top Papers
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- 6MILUV: A Multi-UAV Indoor Localization dataset with UWB and Vision2 citations · 2026
- 7navlie: A Python Package for State Estimation on Lie Groups2 citations · 2023