Nur Aqilah Othman
Papers
11
Total Citations
49
H-Index
5
About
Nur Aqilah Othman is a robotics and control systems researcher whose work centers on mobile robot navigation, localization, and Simultaneous Localization and Mapping (SLAM). Her research focuses on advancing probabilistic estimation techniques — particularly variants of the Kalman filter — to improve the reliability and accuracy of autonomous mobile robots operating in real-world, imperfect environments. Among her most significant contributions is her sustained investigation into the challenges posed by intermittent measurements, where sensor failures or system imperfections cause robots to lose critical observational data. Her 2019 analysis of covariance matrix behavior under such conditions, alongside her earlier foundational work from 2013, helped establish a clearer theoretical understanding of how measurement dropout degrades SLAM performance. She has also addressed partial observability in Extended Kalman Filter navigation, proposing solutions that balance computational cost with estimation accuracy. Her development of a Fuzzy-Extended Kalman Filter (FEKF) demonstrates creative hybridization of soft computing with classical estimation theory to handle unknown noise characteristics. With cumulative citations spanning robotics, control theory, and autonomous systems, and a 2025 review consolidating Kalman filter variants for SLAM, Othman continues to shape both foundational understanding and practical implementation of robust robot navigation frameworks.
Research Focus
Key Achievements
Top Papers
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- 2The impact of cross-correlation on mobile robot localization9 citations · 2015
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