Layth Hamad

Papers

Total Citations

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About

Layth Hamad is a robotics researcher whose work centers on autonomous navigation, simultaneous localization and mapping (SLAM), and intelligent perception systems. His most impactful contribution, "Object Depth and Size Estimation Using Stereo-Vision and Integration With SLAM" (2024, 17 citations), addresses a critical challenge in robotics: enabling autonomous robots to accurately perceive depth and object dimensions without relying solely on expensive LiDAR sensors. By integrating stereo-vision with SLAM, Hamad’s approach enhances robot safety and efficiency in dynamic environments, offering a cost-effective alternative for object identification and localization. His second notable work, "Haris: an Advanced Autonomous Mobile Robot for Smart Parking Assistance" (2024, 6 citations), demonstrates practical application by developing a robot that uses SLAM and license plate recognition to autonomously track vehicles in crowded parking lots, eliminating the need for manual oversight. Together, these papers showcase Hamad’s ability to bridge theoretical SLAM advances with real-world robotic solutions. His research is particularly valuable for students and engineers interested in affordable, vision-based navigation systems for autonomous vehicles and service robots, highlighting a trajectory toward smarter, more accessible robotic technologies.