Daniel Tortei

Centre National de la Recherche Scientifique

Papers

Total Citations

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About

Daniel Tortei is a researcher focused on the intersection of hardware and software co-design, with a particular emphasis on real-time embedded systems for robotics and computer vision. His most notable contribution is the pioneering work on the HW/SW co-design of a visual SLAM (Simultaneous Localization and Mapping) application, a critical technology for autonomous navigation in robots and drones. This 2018 paper, which has garnered 8 citations, addresses the challenge of achieving high-performance visual SLAM on resource-constrained platforms by optimizing the partitioning of algorithms between specialized hardware accelerators and software. Tortei’s approach enables more efficient, low-latency processing, making advanced perception capabilities accessible for compact, power-sensitive devices. While his citation count is modest, his work represents a foundational step in bridging the gap between algorithmic complexity and practical deployment in embedded systems. Tortei’s research is particularly valuable for students and engineers seeking to understand how to tailor computationally intensive vision tasks for real-world, hardware-limited applications, highlighting the importance of co-design in the future of autonomous systems.