Denny Dermawan

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About

Denny Dermawan is a researcher in mobile robotics, with a primary focus on localization and state estimation for autonomous systems operating in unknown environments. His most-cited work, "Mobile Robot Localization via Unscented Kalman Filter" (2019, 10 citations), addresses a fundamental challenge in robotics: accurately estimating a robot’s position and heading without prior environmental knowledge. By applying the Unscented Kalman Filter (UKF)—an advanced alternative to the Extended Kalman Filter—Dermawan demonstrated a more robust approach to handling nonlinear dynamics and noisy sensor data, improving localization reliability in real-world scenarios. This contribution is particularly valuable for autonomous navigation, where precise positioning is critical. While his citation count reflects a focused, early-stage impact, Dermawan’s work has practical implications for fields like warehouse automation, search-and-rescue robotics, and autonomous vehicles. His research underscores the importance of probabilistic filtering in enabling robots to operate safely and efficiently in unstructured settings. For students and researchers exploring sensor fusion or mobile robot autonomy, Dermawan’s study offers a clear, applied example of how UKF outperforms traditional methods, making it a useful reference for those entering the field.