Duan-Yan Hung
Papers
1
Total Citations
4
H-Index
1
About
Duan-Yan Hung is a robotics researcher whose work centers on advancing autonomous navigation through visual Simultaneous Localization and Mapping (SLAM). His primary research focus lies in improving data association—a critical challenge for robots operating in unknown environments—by leveraging robust feature detection techniques. In his most influential work, "Improving Data Association in Robot SLAM with Monocular Vision" (2011), Hung proposed an algorithm that employs Speeded-Up Robust Features (SURF) to enhance the recognition and matching of image features. This contribution directly addresses the problem of maintaining consistent map and pose estimates when a robot relies solely on a single camera, a common constraint in real-world applications. While his citation count of 4 reflects a niche but specialized impact, Hung’s work is notable for its practical approach to a fundamental SLAM bottleneck, offering a method that improves both the reliability and accuracy of monocular vision systems. His research remains relevant for students and engineers seeking efficient solutions to data association in resource-constrained robotic platforms.
Research Focus
Key Achievements
Top Papers
- 1Improving Data Association in Robot SLAM with Monocular Vision4 citations · 2011