Hassan Hajjdiab
University of Ottawa, Concordia University, Abu Dhabi University
Papers
7
Total Citations
63
H-Index
5
About
Hassan Hajjdiab is a researcher whose work bridges robotics, computer vision, and cybersecurity. His primary research areas include multi-robot simultaneous localization and mapping (SLAM), visual obstacle detection, and human-robot interaction. Hajjdiab's most significant contribution is his pioneering work on vision-based multi-robot SLAM, where he developed methods for teams of robots equipped with a single camera to collaboratively map and navigate unknown environments—a foundational paper that has garnered 30 citations. He also advanced autonomous navigation with his wide-baseline obstacle detection algorithm, which enables robots to locate ground-plane obstacles using sparse image views, earning 9 citations. More recently, Hajjdiab has applied his expertise to pressing societal challenges, proposing contactless learning activities using autonomous service robots during the COVID-19 pandemic. Beyond robotics, he has contributed to cybersecurity with novel CAPTCHA systems, including a random image matching approach designed to distinguish humans from bots. His work demonstrates a consistent focus on practical, real-world applications—from enabling robot teams to explore hazardous sites to securing online systems. With a career spanning two decades, Hajjdiab continues to influence both the theoretical foundations and applied technologies of autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1Vision-based multi-robot simultaneous localization and mapping30 citations · 2004
- 2Wide baseline obstacle detection and localization9 citations · 2003
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- 5Random Image Matching CAPTCHA System5 citations · 2017
- 6Simple visual CAPTCHA approach3 citations · 2016
- 7Vision-based robot localization2 citations · 2004