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About

Shaza I. Kaoud Abdelaziz is an emerging researcher specializing in autonomous navigation, multi-sensor fusion, and positioning systems for self-driving vehicles and robotic platforms. Her work addresses one of the most critical challenges in modern navigation: maintaining accurate, reliable positioning when Global Navigation Satellite Systems (GNSS) are unavailable or unreliable, such as in urban canyons or indoor environments. Among her most notable contributions is the development of the NavINST Dataset, a comprehensive multi-sensor collection captured across diverse urban and indoor environments, which serves as a valuable resource for the autonomous navigation research community. Her work on LiDAR registration with high-accuracy 3D digital maps has demonstrated robust positioning solutions in GNSS-challenging scenarios, earning 7 citations. She has also advanced the field through machine learning-based approaches to visual odometry uncertainty estimation, improving the reliability of low-cost integrated navigation systems. Abdelaziz's research on low-cost visual-inertial navigation for mobile robots further highlights her commitment to making autonomous navigation solutions both accessible and practical. With a growing citation record across publications from 2020 to 2025, she represents a promising voice in the next generation of navigation and instrumentation researchers.