Xuankang Wu
Papers
3
Total Citations
9
H-Index
2
About
Xuankang Wu is a researcher advancing the state of the art in autonomous navigation and multi-sensor fusion, with a primary focus on Simultaneous Localization and Mapping (SLAM). His work addresses critical challenges in creating reliable, high-fidelity 3D point cloud maps for autonomous driving and mobile robotics. Wu’s major contributions center on dynamic object removal from LiDAR maps, a persistent problem where moving vehicles and pedestrians leave ghostly traces that degrade localization accuracy. His innovative "Observation Time Difference" (OTD) method offers an online, real-time solution for ground vehicles, effectively filtering out these dynamic artifacts to produce clean, static maps. Complementing this, his work on "EverySync" tackles the foundational issue of precise time synchronization across heterogeneous sensor suites, a necessity for tightly-coupled fusion systems. Though early in his career, his 2024 publications have already garnered citations, signaling the community’s recognition of his practical, hardware-aware approach. By bridging the gap between theoretical SLAM and real-world sensor imperfections, Wu is laying essential groundwork for robust, long-term autonomous operation in dynamic urban environments.
Research Focus
Key Achievements
Top Papers
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