Oualid Djekoune
Papers
7
Total Citations
71
H-Index
5
About
Oualid Djekoune is a leading researcher in mobile robotics, with a primary focus on autonomous navigation, visual odometry, and sensor-based control. His work has been instrumental in advancing how robots perceive and move through unknown environments. Djekoune’s most impactful contribution is his 2014 study evaluating SIFT and SURF performance for monocular visual odometry, which has garnered 26 citations and remains a key reference for feature-matching in robot motion estimation. He also developed the DVFF (Dynamic Virtual Force Field) navigation algorithm, a sensor-based approach for collision-free motion in unmapped spaces, cited 22 times. Earlier foundational work includes the use of optical flow for obstacle detection and Hough transform-based visual guidance to correct robot drift, demonstrating his long-standing expertise in computer vision for robotics. More recently, Djekoune has expanded into environmental monitoring with the AquaRob project, an unmanned surface drone designed for coastal surveillance and pollution detection along Algeria’s 1,622-kilometer coastline. His hybrid localization methods, combining grid matching and extended Kalman filtering, further showcase his versatility in solving real-world robotic challenges. With a career spanning over two decades, Djekoune’s research continues to influence both academic theory and practical robotic systems.
Research Focus
Key Achievements
Top Papers
- 1
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- 3On the Use of Optical Flow in Robot Navigation7 citations · 2007
- 4Visual guidance control based on the Hough transform6 citations · 2002
- 5Localization and guidance with an embarked camera on a mobile robot6 citations · 2002
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- 7