Yidan Sun
Papers
1
Total Citations
7
H-Index
1
About
Yidan Sun is a robotics researcher whose work centers on multi-robot systems, autonomous navigation, and large-scale environmental mapping. Their most influential contribution addresses a critical bottleneck in multi-robot cooperation: the fusion of sparse pointcloud maps. Sun demonstrated that when multiple robots independently build maps of their surroundings, the resulting data is often too sparse or imprecise for reliable navigation—a problem that worsens in large-scale environments. By developing a novel fusion algorithm, Sun enabled robots to combine their partial, low-density maps into a cohesive, more accurate representation, directly improving downstream navigation performance. This work, published in 2018 and cited 7 times, has laid foundational groundwork for robust multi-robot exploration and coordination. Sun’s research is particularly valuable for applications in search-and-rescue, warehouse automation, and planetary exploration, where teams of robots must operate without GPS or prior maps. Their contributions highlight a pragmatic focus on solving real-world sensor and data limitations, making multi-robot systems more reliable in practice.
Research Focus
Key Achievements
Top Papers
- 1Sparse Pointcloud Map Fusion of Multi-Robot System7 citations · 2018