Mohammed Rabah
Papers
2
Total Citations
138
H-Index
2
About
Mohammed Rabah is a researcher at the forefront of autonomous systems and computer vision, with a particular focus on enabling real-time perception for unmanned aerial vehicles (UAVs). His work bridges the gap between artificial intelligence and robotics, developing robust methods for object detection, tracking, and state estimation. Rabah’s most impactful contribution is his pioneering work on integrating convolutional neural networks (CNNs) with the Parrot AR Drone 2, achieving real-time object detection and tracking that significantly advanced autonomous drone navigation. This seminal paper has garnered 126 citations, underscoring its influence in the field. More recently, he has pushed the boundaries of visual odometry by exploring the fusion of event cameras with inertial measurement units (IMUs), a challenging area that promises superior performance in high-speed and low-light conditions. His 2020 study on this topic, which has earned 12 citations, demonstrates his commitment to solving fundamental problems in dynamic visual perception. Through his innovative integration of deep learning and sensor fusion, Rabah is helping to shape the next generation of autonomous, intelligent machines.
Research Focus
Key Achievements
Top Papers
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- 2