Papers
23
Total Citations
1,404
H-Index
15
About
Yun Chang is a robotics researcher whose work centers on simultaneous localization and mapping (SLAM), autonomous exploration, and spatial perception for robotic systems operating in challenging real-world environments. His most significant contributions lie in developing robust, scalable SLAM systems capable of functioning in perceptually degraded and GPS-denied settings, including subterranean environments such as tunnels, mines, and caves. Chang's research has produced landmark systems including LAMP and LAMP 2.0, which tackle large-scale underground mapping for multi-robot teams, and the Kimera-Multi framework, a pioneering distributed multi-robot metric-semantic SLAM system with over 300 cumulative citations across its publications. His work on Hydra introduced real-time 3D scene graph construction, pushing robotic perception toward richer hierarchical environmental understanding. More recently, Clio extended this vision to open-set semantics, enabling task-driven scene representations unconstrained by predefined object categories. A defining highlight of Chang's career is his involvement with TEAM CoSTAR, which won Phase II of the prestigious DARPA Subterranean Challenge—a landmark achievement in autonomous robotics. His body of work, accumulating over 1,200 citations, reflects both theoretical depth and practical engineering impact, making him a notable voice in advancing perception and autonomy for robots deployed in the most demanding operational conditions.
Research Focus
Key Achievements
Top Papers
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- 3Present and Future of SLAM in Extreme Environments: The DARPA SubT Challenge176 citations · 2023
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- 10<i>Clio:</i> Real-Time Task-Driven Open-Set 3D Scene Graphs35 citations · 2024