Xiaoping Wu
Papers
2
Total Citations
11
H-Index
2
About
Xiaoping Wu is a robotics and autonomous systems researcher whose work centers on simultaneous localization and mapping (SLAM) and mobile robot navigation. Wu has made notable contributions to the development of distributed SLAM architectures, advancing the field by addressing key limitations of conventional approaches. His 2019 paper introduces a decorrelated distributed Extended Kalman Filter (EKF-SLAM) system that innovatively combines the strengths of both EKF-SLAM and distributed SLAM frameworks, structuring each subsystem around individually observed landmarks to improve computational efficiency and navigation accuracy — a work that has garnered 9 citations. Building on earlier research, Wu's 2016 contribution tackles the persistent challenge of particle impoverishment in particle filter-based SLAM systems operating in high-dimensional environments, proposing a particle swarm optimization strategy to enhance the reliability of distributed SLAM for mobile robot navigation. Together, these works reflect Wu's sustained focus on making SLAM systems more robust, scalable, and practically deployable in real-world autonomous navigation scenarios. His research offers valuable insights for engineers and researchers working at the intersection of probabilistic robotics, state estimation, and intelligent autonomous systems.
Research Focus
Key Achievements
Top Papers
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