Papers
6
Total Citations
53
H-Index
3
About
Redouane Khemmar is a leading researcher in autonomous navigation, computer vision, and intelligent mobility systems, with a focus on real-time perception for robotics and assistive technologies. His most cited work, “Benchmark of Visual SLAM Algorithms: ORB-SLAM2 vs RTAB-Map” (30 citations), provides a critical comparative analysis of state-of-the-art visual simultaneous localization and mapping (vSLAM) algorithms using Intel RealSense cameras, establishing foundational benchmarks for robotic localization. Khemmar has made significant contributions to deep learning-based object detection and tracking, notably in “Real Time Pedestrian and Object Detection and Tracking-based Deep Learning. Application to Drone Visual Tracking” (9 citations), demonstrating embedded vision solutions for aerial platforms. His innovative self-supervised sidewalk perception system for robotic wheelchairs (6 citations) advances smart mobility for disabled individuals, integrating fast video semantic segmentation to enable safe autonomous navigation in urban environments. Khemmar’s work also spans multi-sensor fusion for face detection and recognition, combining omnidirectional and PTZ cameras, and ROS-based autonomous wheelchair navigation. With a research portfolio emphasizing real-time, embedded vision systems, Khemmar’s impact is evident in his development of practical, deployable solutions that bridge computer vision, robotics, and assistive technology, making autonomous mobility more accessible and reliable.
Research Focus
Key Achievements
Top Papers
- 1Benchmark of Visual SLAM Algorithms: ORB-SLAM2 vs RTAB-Map30 citations · 2019
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- 6ROS-based Autonomous Navigation Wheelchair using Omnidirectional Sensor2 citations · 2016