Seoungjae Cho
Papers
14
Total Citations
99
H-Index
6
About
Seoungjae Cho is a robotics and autonomous systems researcher whose work spans LiDAR-based perception, 3D reconstruction, and human-robot interaction. His research is particularly focused on enabling robots to understand and navigate complex real-world environments, with ground segmentation emerging as a defining contribution to the field. His most-cited work, "Enhanced Ground Segmentation Method for LiDAR Point Clouds in Human-Centric Autonomous Robot Systems" (2019, 21 citations), introduced efficient techniques for separating traversable terrain from obstacles — a foundational step in autonomous navigation pipelines. Complementing this, his range image-based DBSCAN clustering method (2018, 13 citations) advanced point cloud processing for real-time robotic perception tasks. Cho has also made significant strides in cloud-based robotics, proposing a multi-robot 3D reconstruction framework (2017, 14 citations) that addresses the computational demands of real-time environmental mapping. His earlier work on traversable ground segmentation for mobile mapping (2014) demonstrated a consistent long-term interest in practical robot control applications. Spanning ubiquitous network environments, reinforcement learning for human-robot interaction, and photorealistic 3D modeling, Cho's diverse yet cohesive body of research positions him as a versatile contributor to the growing field of intelligent autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1
- 23D Reconstruction Framework for Multiple Remote Robots on Cloud System14 citations · 2017
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- 5Adaptive ground segmentation method for real-time mobile robot control7 citations · 2017
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