Ayoub Mamri
Papers
1
Total Citations
3
H-Index
1
About
Ayoub Mamri is a researcher at the forefront of embedded computer vision and hardware acceleration, with a focus on making advanced robotic perception accessible on low-cost platforms. His most-cited work, "High-Level Synthesis Implementation of Monocular SLAM on Low-Cost Parallel Platforms" (2021), demonstrates a pivotal contribution: the efficient mapping of computationally intensive monocular SLAM algorithms onto FPGA-based systems using high-level synthesis. This achievement bridges the gap between complex visual odometry and resource-constrained devices, enabling real-time localization and mapping without expensive GPUs. With over 3 citations, this paper highlights his ability to optimize algorithmic performance for parallel hardware, a critical step toward democratizing autonomous navigation in drones, mobile robots, and IoT devices. Mamri’s research integrates hardware-software co-design, computer vision, and reconfigurable computing, offering practical solutions for energy-efficient, real-time perception. His work is particularly valuable for students and engineers seeking to deploy SLAM on embedded systems, showcasing how high-level synthesis can accelerate development while maintaining accuracy. By tackling the bottleneck of computational cost, Mamri is shaping the future of affordable, intelligent robotics.
Research Focus
Key Achievements
Top Papers
- 1