Dee Meng Kang

Agency for Science, Technology and Research

Papers

Total Citations

H-Index

About

Dee Meng Kang is a researcher whose work has centered on advancing localization and navigation technologies for autonomous systems, particularly in indoor environments. His major contribution lies in developing a hybrid localization system architecture that integrates Real-time Locating Systems (RTLS) with dead reckoning (DR) methods for mobile robots. This approach, detailed in his most-cited paper from 2010, addresses the critical challenge of cumulative error in DR systems by fusing them with RTLS data, offering a cost-effective and robust solution for indoor robot navigation. While his citation count remains modest, his work represents an early and practical step toward scalable indoor positioning, a foundational problem in robotics and automation. Kang’s research is notable for its focus on balancing simplicity, cost, and accuracy, making it relevant for applications in warehouse logistics, service robots, and smart environments. His contributions underscore the importance of sensor fusion in overcoming the limitations of individual localization techniques, providing a framework that continues to inform developments in autonomous indoor navigation.