Jieying Lu

Papers

Total Citations

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About

Jieying Lu’s research centers on multi-robot systems, with a particular focus on cooperative localization and state estimation. Her work addresses the fundamental challenge of enabling robot teams to determine their positions accurately when relying only on local sensors and neighbor-to-neighbor communication. In her most cited paper (10 citations), Lu developed a two-stage Extended Kalman Filter (EKF) based algorithm for a leader-follower robot team, where followers first estimate their relative pose to a landmark before fusing this with inter-robot measurements. This approach significantly improves localization robustness in GPS-denied environments. Her follow-up work (3 citations) extended this to a fully distributed framework, allowing each robot to process only local data while still achieving global consistency through wireless data exchange. Lu’s contributions are particularly valuable for swarm robotics and search-and-rescue operations, where centralized infrastructure is unavailable. By tackling the core trade-off between communication constraints and estimation accuracy, her research provides practical algorithms that scale to larger teams. Her work continues to influence the design of decentralized perception systems in field robotics.