Sofianita Mutalib

Universiti Teknologi MARA System, Universiti Teknologi MARA

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About

Driven by the pressing need for robust perception in autonomous systems, Sofianita Mutalib’s research focuses on the intersection of deep learning, sensor fusion, and object detection for autonomous vehicles and mobile robots. Her work directly addresses the critical limitations of camera-based vision in low-light conditions, as demonstrated in her most-cited paper, “Object Detection for Autonomous Vehicle with LiDAR Using Deep Learning” (21 citations). This study pioneers the use of LiDAR data with deep learning algorithms to maintain reliable detection when traditional visual sensors fail. Expanding on this, her comprehensive review on object detection for autonomous mobile robots (10 citations) synthesizes the field’s progress, highlighting the role of AI in advancing human-robot interaction. Her foundational work in “Towards Modelling Autonomous Mobile Robot Localization by Using Sensor Fusion Algorithms” (7 citations) tackles the challenge of achieving centimetre-level localization in crowded environments through low-cost sensor fusion. By integrating LiDAR-based detection with robust localization techniques, Mutalib’s contributions are paving the way for safer, more reliable autonomous navigation—a cornerstone for the next generation of intelligent transportation and robotics.