Papers
7
Total Citations
38
H-Index
3
About
Mohamed Abouzahir is a leading researcher in autonomous navigation and robotics, specializing in the real-time implementation of Simultaneous Localization and Mapping (SLAM) algorithms on embedded and low-power architectures. His major contributions center on accelerating monocular FastSLAM2.0—a Rao-Blackwellized particle filter-based approach—for large-scale environments, demonstrating that complex SLAM can run efficiently on resource-constrained platforms like OMAP embedded systems and FPGAs. His work on heterogeneous architectures, including OpenCL and OpenGL implementations on high-end GPUs, has pushed the boundaries of computational efficiency, achieving real-time performance with many particles. With over 38 citations across his most-cited papers, Abouzahir’s research has directly addressed the critical challenge of balancing localization accuracy with computational cost, as seen in his 2024 study on UKF SLAM for autonomous driving, which enhances perception and navigation for area coverage. His notable achievements include pioneering high-level synthesis for FPGA-based SLAM and systematically evaluating low-power embedded architectures, making him a key figure in enabling reliable, cost-effective autonomous systems for real-world applications.
Research Focus
Key Achievements
Top Papers
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- 7High-level synthesis for FPGA design based-SLAM application2 citations · 2016