Xiangxi Bu

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About

Xiangxi Bu is a robotics researcher specializing in multi-robot perception, cooperative localization, and dense reconstruction for infrastructure-free and GNSS-denied environments. His work addresses a fundamental challenge in multi-robot systems: achieving accurate, drift-free relative pose estimation without relying on external infrastructure or inter-robot visual overlap. Bu’s major contributions include the development of UWB-VIO fusion algorithms that significantly improve the robustness of relative localization for ground robot teams, as demonstrated in his highly cited 2022 paper (29 citations). He further advanced the field with a range-aided cooperative localization and consistent reconstruction framework (2023, 9 citations), enabling drift-free performance even when robots have no common field of view. His most recent work (2024, 4 citations) extends these principles to LiDAR-based systems using sliding window graph optimization, pushing the boundaries of cooperative mapping in unknown environments. Bu’s research is notable for its practical impact on real-world multi-robot exploration and perception tasks, offering scalable solutions that are critical for applications in search-and-rescue, underground mining, and planetary exploration.