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About

Kamarulzaman Kamarudin is a robotics and autonomous systems researcher whose work centers on mobile robot navigation, Simultaneous Localization and Mapping (SLAM), mobile olfaction, and intelligent sensing. He has made significant contributions to advancing indoor mapping capabilities, particularly through his pioneering investigations into depth sensor integration with SLAM frameworks. His widely cited 2013 and 2014 studies on converting Microsoft Kinect 3D depth data for 2D SLAM applications — accumulating over 70 combined citations — helped establish cost-effective alternatives to traditional laser rangefinders in robotic navigation. His 2019 analysis of GMapping-based SLAM parameter optimization further refined practical implementation standards for autonomous mobile systems. Beyond navigation, Kamarudin has distinguished himself in mobile olfaction research, exploring gas source localization using novel graphene-based sensors and examining the cross-sensitivity of metal oxide sensors to environmental conditions — work with meaningful implications for hazardous environment monitoring. His more recent research extends into reinforcement learning-driven path planning and multi-domain airflow modeling, reflecting a forward-looking research trajectory. His exploration of confined space robotics and behavior-based robotic systems further underscores a sustained commitment to safety-critical applications. With over 200 cumulative citations, Kamarudin's body of work represents a valuable bridge between sensing technology, autonomous navigation, and real-world robotic deployment.