Shiqin Sun
Papers
2
Total Citations
9
H-Index
2
About
Shiqin Sun is a robotics researcher whose work focuses on the critical challenge of collaborative mapping in multi-robot systems. Her primary research areas include simultaneous localization and mapping (SLAM), multi-robot coordination, and sparse point-cloud map fusion. Sun’s major contribution lies in developing strategies for merging environmental maps created by multiple robots operating in large-scale, unknown environments. In her most cited work, "Sparse Pointcloud Map Fusion of Multi-Robot System" (2018, 7 citations), she addresses the core issue of how poor map building degrades navigation performance in multi-robot teams. Building on this, her 2021 paper "Online map fusion system based on sparse point-cloud" (2 citations) proposes a centralized architecture for real-time map merging using a two-robot system. While her citation counts are modest, Sun’s work tackles a fundamental bottleneck in deploying robot teams for autonomous exploration and search-and-rescue operations. Her research is particularly notable for emphasizing practical, online solutions over offline batch processing, making her contributions valuable for real-world multi-robot deployments where timely map fusion is essential for effective navigation.
Research Focus
Key Achievements
Top Papers
- 1Sparse Pointcloud Map Fusion of Multi-Robot System7 citations · 2018
- 2Online map fusion system based on sparse point-cloud2 citations · 2021