Kaoutar Dahmane

Papers

Total Citations

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About

Kaoutar Dahmane is a researcher whose work bridges the critical intersection of robotics, computer vision, and heterogeneous computing. Her primary research focus lies in Simultaneous Localization and Mapping (SLAM) algorithms—the foundational technology enabling autonomous systems to navigate unknown environments. Dahmane’s most impactful contribution, the paper "SLAM Algorithm: Overview and Evaluation in a Heterogeneous System" (2021), provides a comprehensive analysis of SLAM performance across diverse hardware platforms, including CPUs, GPUs, and FPGAs. This work is essential for optimizing real-time navigation in resource-constrained robotic systems, such as drones and autonomous vehicles. With 6 citations, this study has already informed subsequent research in efficient, hardware-aware SLAM implementations. Dahmane’s expertise in evaluating algorithmic trade-offs on heterogeneous architectures positions her as a key voice in advancing practical, deployable autonomy. Her work not only clarifies the strengths and limitations of popular SLAM methods but also offers a roadmap for future system design, making her research invaluable for students and engineers seeking to build robust, real-world robotic systems.