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About

Narongdech Keeratipranon is a robotics researcher whose work centers on mobile robot navigation, localization, and mapping — particularly in the challenging domain of bearing-only and vision-based systems. His research addresses fundamental questions about how robots perceive and navigate unknown environments using minimal or low-cost sensor configurations. Among his most notable contributions is his work on manifold learning for robot navigation, where he pioneered the use of Self-Organizing Maps (SOMs) as isometric representations of sensor data, enabling robots to build geometrically meaningful maps from raw readings. His investigations into Simultaneous Localization and Mapping (SLAM) are particularly significant, including methods that rely solely on landmark bearing measurements extracted from panoramic views along linear trajectories — reducing hardware dependency while maintaining navigational accuracy. His uncertainty analysis of landmark initialization further strengthened the theoretical foundations of bearing-only SLAM systems. Keeratipranon also contributed to the development of bearing similarity measures for self-organizing feature maps, advancing the mathematical tools available for spatial reasoning in robotics. With citations across multiple publications from 2005 to 2007, his body of work has informed subsequent research in autonomous robot navigation and low-cost vision-based mapping systems, establishing him as a meaningful contributor to the field during a formative period in mobile robotics research.