Bao Lam Dang

Hanoi University of Science and Technology, Lockheed Martin (United States)

Papers

Total Citations

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About

Bao Lam Dang is a researcher whose work spans robotics, autonomous navigation, and brain-machine interfaces, with a focus on real-time sensor processing and embedded systems. In his most-cited paper, "A Sensor Fusion Approach for Improving Implementation Speed and Accuracy of RTAB-Map Algorithm Based Indoor 3D Mapping" (2023, 5 citations), Dang addresses critical challenges in indoor 3D mapping—such as environmental complexity and robot positioning errors—by enhancing the speed and accuracy of the RTAB-Map algorithm through sensor fusion. This contribution has practical implications for industries relying on autonomous navigation and robotics. Earlier, Dang contributed to neural engineering with "A Portable Wireless DSP System for a Brain Machine Interface" (2005, 3 citations), where he designed a wearable digital signal processing system capable of translating neural signals into motor commands, enabling real-time prediction model training via a high-speed data bus. This work highlights his versatility in bridging hardware and algorithmic innovation. With a total of 8 citations across his most-cited works, Dang’s research demonstrates a commitment to advancing real-time, embedded solutions for both robotic mapping and neuroprosthetic applications, making his contributions valuable for students and researchers interested in sensor fusion, DSP, and autonomous systems.