Ahmet Saglam
Papers
2
Total Citations
4
H-Index
2
About
Ahmet Saglam is a robotics researcher specializing in autonomous mobile robot navigation, sensor simulation, and real-time environmental perception. His work focuses on enabling robots to intelligently interpret indoor environments for safe and efficient movement. In his highly cited paper, "Realtime Corridor Detection for Mobile Robot Navigation with Hough Transform Using a Depth Camera," Saglam introduced a novel method that leverages a single depth camera to detect corridor-like structures in real time, even in cluttered or partially obstructed spaces—a critical capability for indoor navigation. This contribution has garnered 2 citations, reflecting its practical relevance. Additionally, his research on "Scalability of Sensor Simulation in ROS-Gazebo Platform with and without Using GPU" addresses the pressing need for efficient simulation tools in robotics. By analyzing how GPU acceleration impacts the scalability of sensor simulation, Saglam provides key insights for developers seeking to test perception and movement strategies in complex virtual environments. His work bridges the gap between theoretical simulation and real-world deployment, making him a notable contributor to the field of autonomous robotics.
Research Focus
Key Achievements
Top Papers
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- 2