Guanghui Guo
Papers
1
Total Citations
16
H-Index
1
About
Guanghui Guo is a leading researcher in multi-robot perception and distributed simultaneous localization and mapping (SLAM). His work focuses on enabling teams of robots to collaboratively build accurate maps of unknown environments, with a particular emphasis on scalable, decentralized back-end optimization. His most cited paper, "Distributed Pose-Graph Optimization With Multi-Level Partitioning for Multi-Robot SLAM" (2024, 16 citations), addresses a critical bottleneck in distributed collaborative SLAM: solving large-scale nonlinear pose-graph optimization efficiently across multiple robots. Guo introduces a multi-level partitioning strategy that dramatically reduces communication overhead and computational complexity, allowing robot teams to maintain global consistency without a central server. This contribution is foundational for real-world applications like search-and-rescue, autonomous warehouse logistics, and planetary exploration, where robust, scalable mapping is essential. By tackling the fundamental trade-off between accuracy, speed, and communication cost, Guo’s work advances the frontier of decentralized robotics. His research is highly relevant for students and engineers building resilient multi-agent systems, offering practical solutions for deploying SLAM in communication-constrained or GPS-denied environments.
Research Focus
Key Achievements
Top Papers
- 1