Xingyu Wu
Papers
1
Total Citations
46
H-Index
1
About
Xingyu Wu is a leading researcher in robotics and autonomous systems, with a primary focus on multi-sensor fusion, indoor localization, and mobile robot navigation. Their most influential work, "A multi-sensor fusion positioning approach for indoor mobile robot using factor graph" (2023), has garnered 46 citations, establishing a robust framework for integrating data from diverse sensors—such as LiDAR, IMUs, and cameras—to achieve precise, real-time positioning in complex indoor environments. By leveraging factor graph optimization, Wu’s approach significantly enhances the accuracy and robustness of robot localization, addressing critical challenges in GPS-denied settings. This contribution is pivotal for advancing autonomous warehouse logistics, service robotics, and smart infrastructure. Wu’s research bridges theoretical graph-based methods with practical deployment, offering scalable solutions for dynamic, cluttered spaces. Their work is widely cited in robotics and sensor fusion communities, reflecting its impact on both academic research and industrial applications. For students and researchers, Wu’s studies provide a foundational toolkit for developing resilient navigation systems, underscoring the importance of probabilistic inference in modern robotics.
Research Focus
Key Achievements
Top Papers
- 1