Papers
2
Total Citations
12
H-Index
1
About
Hou Yi is a researcher specializing in multi-robot systems, with a focus on decentralized cooperative localization and mutual perception. His work addresses fundamental challenges in enabling teams of autonomous robots to accurately estimate their relative positions without relying on centralized infrastructure. His most-cited paper, "Graph-based observability analysis for mutual localization in multi-robot systems" (2022, 11 citations), introduces a novel framework for analyzing when and how robots can reliably determine each other's poses through local observations, providing critical theoretical foundations for scalable swarm coordination. He further advanced the field with "Consistent batch fusion for decentralized multi-robot cooperative localization" (2024), which develops efficient algorithms for fusing distributed sensor data while maintaining estimation consistency—a key requirement for real-world deployment. Yi's contributions bridge graph theory and estimation theory, offering practical tools for applications ranging from search-and-rescue operations to autonomous warehouse logistics. His work is particularly notable for addressing the observability problem in dynamic, multi-agent settings, a persistent bottleneck in robotics research. With a growing citation impact, Hou Yi is establishing himself as a rising authority in decentralized robotic systems.
Research Focus
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Top Papers
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