Chule Yang
Papers
10
Total Citations
316
H-Index
9
About
Chule Yang is a robotics and autonomous systems researcher whose work centers on collaborative mapping, sensor fusion, and multi-robot perception in complex environments. His research addresses one of the most pressing challenges in autonomous systems: enabling teams of robots to build accurate, semantically rich representations of their surroundings by integrating data from diverse and heterogeneous sensors. Yang's most impactful contribution is his development of hierarchical probabilistic frameworks for collaborative 3D mapping and semantic understanding. His 2020 paper on collaborative semantic mapping has garnered 59 citations, reflecting the field's appetite for approaches that move beyond purely geometric representations toward context-aware scene understanding. His multilevel fusion systems for merging sparse and dense maps from heterogeneous sensors — cited 42 times — and his hierarchical probabilistic map-matching framework from 2018 (44 citations) together establish a coherent methodological thread running through his career. Notably, Yang has also tackled practical sensing challenges, including a widely adopted two-step extrinsic calibration method between sparse LiDAR and thermal cameras (38 citations), enabling day-and-night robotic operation. Across more than 300 cumulative citations, his body of work has meaningfully advanced the capabilities of cooperative autonomous systems operating in unstructured, real-world conditions.
Research Focus
Key Achievements
Top Papers
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- 6A Hierarchical Framework for Collaborative Probabilistic Semantic Mapping31 citations · 2020
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- 9Probabilistic Fusion Framework for Collaborative Robots 3D Mapping10 citations · 2018
- 10Probabilistic 3D Semantic Map Fusion Based on Bayesian Rule6 citations · 2019