Papers
9
Total Citations
446
H-Index
5
About
Xudou Zou is a leading researcher in multi-robot perception, sensor fusion, and robust localization for autonomous systems. His most impactful contribution is **Dynamic-SLAM** (344 citations), a seminal work that integrates deep learning with semantic monocular visual SLAM to enable reliable localization in dynamic, real-world environments—a critical advancement over traditional static-scene assumptions. Zou’s research fundamentally addresses the challenge of accurate relative pose estimation for robot teams, pioneering methods like **UWB-VIO fusion** (29 citations) and **range-aided cooperative localization** (9 citations) that achieve drift-free, infrastructure-free positioning. He has also advanced sensor calibration with **FDO-Calibr** (4 citations) and explored deep learning for odometry using CNN-RNN architectures (3 citations). His work on **MEMS-IMU performance evaluation** (9 citations) provides essential benchmarks for visual-inertial navigation. By tackling the core problems of multi-robot coordination, Zou’s innovations enable scalable, precise, and robust perception for applications ranging from search-and-rescue to automated guided vehicles.
Research Focus
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Top Papers
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- 8Range-Aided LiDAR Cooperative Localization and Mapping4 citations · 2024
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