Papers
9
Total Citations
151
H-Index
7
About
Sunghwan Ahn is a robotics researcher specializing in Simultaneous Localization and Mapping (SLAM), mobile robot navigation, and sensor fusion. His work has made significant contributions to enabling autonomous robots to reliably perceive and navigate real-world environments, particularly domestic settings and humanoid platforms. Ahn's most impactful contributions center on developing robust SLAM solutions that integrate multiple sensing modalities. His 2006 work on visual object recognition for EKF-SLAM (40 citations) advanced data association reliability in home environments, while his complementary research on sonar-based feature detection (36 citations) addressed the practical challenges of angular uncertainty and specular reflections inherent to acoustic sensing. Recognizing the limitations of single-sensor approaches, he pioneered sensor fusion strategies combining vision and sonar to exploit their complementary strengths, as demonstrated in his metric SLAM framework (19 citations). Ahn also extended his expertise to humanoid robotics, tackling the unique challenge of 3D visual SLAM during dynamic, heel-toe walking patterns on the Roboray platform (22 citations). His earlier work on Generalized Voronoi Graph construction further contributed to practical guide robot navigation under partial sensor coverage. Collectively, his publications reflect a career dedicated to bridging theoretical SLAM advances with deployable, real-world robotic systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2Robust sonar feature detection for the SLAM of mobile robot36 citations · 2005
- 3On-board odometry estimation for 3D vision-based SLAM of humanoid robot22 citations · 2012
- 4Metric SLAM in Home Environment with Visual Objects and Sonar Features19 citations · 2006
- 5A Practical Solution to SLAM and Navigation in Home Environment11 citations · 2006
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- 8
- 9Robust feature detection using sonar sensors for mobile robots2 citations · 2005