Alireza Bab‐Hadiashar
RMIT University, Swinburne University of Technology, MIT University
Papers
9
Total Citations
631
H-Index
6
About
Alireza Bab-Hadiashar is a leading figure in robotics and computer vision, whose work bridges fundamental theory with transformative real-world applications. His research spans visual odometry and SLAM, reactive robot navigation, and the development of intelligent robotic systems for manufacturing, infrastructure inspection, and rehabilitation. His highly cited overview of visual odometry and SLAM (308 citations) has become a key reference for mobile robotics, while his comparative study of reactive chemotaxis algorithms (265 citations) remains foundational in bio-inspired robot navigation. Bab-Hadiashar has also pioneered impactful engineering solutions, including the cable-driven CarNeck rehabilitation robot for patients with Dropped Head Syndrome, and advanced in-process 4D reconstruction for robotic additive manufacturing. His work on deep learning for storm-water pipe inspection and autonomous hyperspectral characterization of polymer degradation demonstrates a commitment to automating critical infrastructure and materials science. With a career spanning foundational statistical methods for computer vision to cutting-edge robotic hand-eye calibration, Bab-Hadiashar’s contributions consistently push the boundaries of autonomous systems, earning him recognition as a researcher who translates complex theory into practical, high-impact technologies.
Research Focus
Key Achievements
Top Papers
- 1An Overview to Visual Odometry and Visual SLAM: Applications to Mobile Robotics308 citations · 2015
- 2A comparison of reactive robot chemotaxis algorithms265 citations · 2003
- 3A Novel Design of Cable-Driven Neck Rehabilitation Robot (CarNeck)17 citations · 2019
- 4In-process 4D reconstruction in robotic additive manufacturing14 citations · 2024
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- 9Implementing a robotic design and build exercise2 citations · 2001