Mohamed Abdel‐Nasser
Papers
1
Total Citations
12
H-Index
1
About
Mohamed Abdel-Nasser is a leading researcher at the intersection of computer vision, deep learning, and mobile robotics, with a particular focus on enabling intelligent perception in resource-constrained environments. His work centers on developing efficient semantic mapping approaches that allow mobile robots to understand and navigate their surroundings using only monocular vision—a critical capability for cost-effective and lightweight autonomous systems. His most-cited paper, "Efficient deep learning-based semantic mapping approach using monocular vision for resource-limited mobile robots" (2022), has already garnered 12 citations, reflecting its timely impact on the robotics community. Abdel-Nasser’s contributions are notable for bridging the gap between high-accuracy deep learning models and the computational limitations of embedded platforms, advancing practical deployment in real-world scenarios such as search-and-rescue, agricultural monitoring, and domestic service robots. His work is distinguished by its emphasis on algorithmic efficiency without sacrificing semantic understanding, making him a key figure in the push toward accessible, intelligent robotics.
Research Focus
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Top Papers
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