Papers
5
Total Citations
115
H-Index
3
About
Yusra Alkendi is an emerging robotics and computer vision researcher whose work spans autonomous navigation, neuromorphic sensing, and tactile perception for robotic systems. Her most cited contribution, "State of the Art in Vision-Based Localization Techniques for Autonomous Navigation Systems" (2021, 87 citations), established her as a notable voice in visual odometry and visual-inertial odometry, providing the research community with a comprehensive roadmap of localization methods critical to fully autonomous platforms. Building on this foundation, Alkendi has pioneered the application of neuromorphic event cameras to robotics, developing graph-based deep learning architectures that harness the asynchronous, high-temporal-resolution nature of these sensors for tasks including motion segmentation, panoptic scene understanding, and dynamic obstacle localization in challenging low-light environments. Her work on TactiGraph (2023, 18 citations) further demonstrates her interdisciplinary reach, introducing asynchronous graph neural networks to vision-based tactile sensing, enabling robots to perceive contact with greater precision. Across her portfolio, Alkendi consistently bridges cutting-edge sensor technologies with novel neural network designs, positioning her as a promising researcher shaping the future of intelligent, perception-driven robotic systems.
Research Focus
Key Achievements
Top Papers
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