Mouna Baklouti
Papers
1
Total Citations
3
H-Index
1
About
Mouna Baklouti’s research centers on embedded systems, mobile robotics, and efficient sensor fusion, with a particular focus on low-cost, real-time localization solutions. Her most-cited work, “Efficient embedded software implementation of a low cost robot localization system” (2019), tackles a critical gap in robotics: the challenge of implementing accurate localization on resource-constrained hardware without relying on expensive sensors or computationally heavy methods like Kalman filters. By proposing a lightweight, embedded-friendly approach, Baklouti demonstrates how to achieve reliable robot positioning using minimal resources—a contribution that directly impacts the scalability and affordability of autonomous systems. While her citation count is still growing, her work stands out for its practical, implementation-driven perspective, bridging the gap between theoretical algorithms and real-world deployment. Baklouti’s research is especially relevant for students and engineers working on cost-sensitive robotics projects, embedded AI, or edge computing, where efficiency and hardware constraints are paramount. Her focus on low-cost, accessible solutions positions her as a key voice in making autonomous navigation more widely adoptable.
Research Focus
Key Achievements
Top Papers
- 1