Hsun-Hao Chang
Papers
2
Total Citations
40
H-Index
2
About
Hsun-Hao Chang is a robotics researcher whose primary contributions lie in Simultaneous Localization and Mapping (SLAM) for indoor environments, with a particular focus on efficiency and practical deployment. His most influential work, "A Light-and-Fast SLAM Algorithm for Robots in Indoor Environments Using Line Segment Map" (2011), has garnered 36 citations—a strong indicator of its impact in the field. In this paper, Chang introduced a lightweight Rao-Blackwellized approach that dramatically reduces computational complexity and memory usage, addressing a critical bottleneck in SLAM systems. This innovation enables robots to navigate accurately without requiring high-end hardware, making it especially valuable for cost-sensitive applications. Chang further advanced the field through his work on stereo vision-based SLAM (2010), where he developed a system using 3D vertical line landmarks extracted from stereo cameras. By employing two distinct observation models to manage measurement uncertainty, he enhanced robustness in cluttered indoor settings. Together, these contributions establish Chang as a key figure in making SLAM more accessible and efficient for real-world robotic navigation.
Research Focus
Key Achievements
Top Papers
- 1
- 2SLAM for Indoor Environment Using Stereo Vision4 citations · 2010