Papers
1
Total Citations
3
H-Index
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About
Omid Panah is a researcher in robotics and autonomous systems, with a focus on improving Simultaneous Localization and Mapping (SLAM) for mobile robots operating in unknown indoor environments. His most cited work, "Enhanced SLAM for Autonomous Mobile Robots using Unscented Kalman Filter and Neural Network" (2015), introduces a novel approach that integrates an optimized Unscented Kalman Filter (UKF) with a Radial Basis Function (RBF) neural network. This method significantly reduces estimation errors in SLAM, enhancing the accuracy and reliability of autonomous navigation. While his citation count is modest, with 3 citations for this key paper, Panah’s contribution lies in advancing sensor fusion and adaptive filtering techniques, addressing critical challenges in real-world robotics. His work demonstrates a practical synthesis of control theory and machine learning, offering a foundation for future developments in robust autonomous systems. Panah’s research is particularly relevant for engineers and students exploring efficient, error-tolerant SLAM solutions in constrained environments.
Research Focus
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Top Papers
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