Baowang Lian

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About

Baowang Lian is a researcher advancing the frontiers of multi-robot systems and indoor localization. His primary research focuses on resilient cooperative localization, addressing the critical challenge of enabling multiple robots to accurately determine their positions in dynamic, GPS-denied environments. Lian’s major contributions include developing decentralized algorithms that robustly fuse information from multiple sources—such as odometry, ranging measurements, and inertial sensors—while mitigating the effects of faulty or malicious data. His 2023 paper on resilient decentralized cooperative localization, with 19 citations, proposes a novel framework that tracks interdependencies and adapts to diverse measurement models, significantly improving accuracy over traditional methods. In indoor positioning, Lian introduced Amp-Phi (2018, 11 citations), a system that leverages Channel State Information (CSI) amplitude and phase for fingerprinting, outperforming conventional RSSI-based approaches that are prone to noise. His 2024 work on factor graph-based resilient localization further enhances multi-robot cooperation under GNSS-denied conditions. With a growing citation impact and a focus on practical, robust solutions, Lian’s work is pivotal for autonomous swarms, warehouse logistics, and search-and-rescue operations.