Papers
2
Total Citations
6
H-Index
1
About
Hany Ragab is a researcher advancing the reliability and autonomy of mobile robotic systems, with key contributions in sensor fusion, navigation, and real-time manipulation. His most cited work, "Machine Learning-based Visual Odometry Uncertainty Estimation for Low-cost Integrated Land Vehicle Navigation" (2020, 5 citations), tackles a critical challenge in autonomous navigation: ensuring robust positioning when GPS signals are unreliable. By integrating inertial navigation systems with machine learning-driven uncertainty estimation for visual odometry, Ragab enhances the safety and performance of low-cost platforms—a vital step toward practical deployment in robotics and autonomous vehicles. His more recent study, "Real-Time Object Detection and Grasping with an Autonomous Mobile Robot: A Case Study" (2024, 1 citation), demonstrates a complete pipeline for pick-and-place and hold-and-run missions, combining a four-wheeled chassis with an articulated arm to manipulate objects of specific dimensions. This work showcases his ability to bridge perception and action in real-world settings. Though early in his career, Ragab’s focus on cost-effective, learning-enhanced navigation and manipulation positions him as a promising contributor to the field, with work that directly addresses the gap between theoretical algorithms and operational autonomy.
Research Focus
Key Achievements
Top Papers
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- 2