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This paper presents a framework for multi-robot Pose Graph Optimization (PGO) that enables on-demand information exchange through Active Rendezvous and mitigates outlier measurements using Channel State Information (CSI). The proposed method significantly reduces pose estimation errors, achieving a 64% reduction in ground truth pose error and a 32% reduction in outlier impact. The approach is distributed, environment-independent, and applicable in challenging conditions where traditional sensors may fail.

Author

huyahuicumtb

Language

EN