Papers
5
Total Citations
38
H-Index
3
About
Zirui Wu is a rising researcher at the forefront of robotics and autonomous systems, with a focus on large-scale 3D perception, multi-robot coordination, and urban mobility. His most impactful work, "LATITUDE," introduces a novel approach to robotic global localization using Neural Radiance Fields (NeRFs) with a truncated dynamic low-pass filter, enabling city-scale pose estimation without initial predictions—a breakthrough that has already garnered 23 citations since 2023. Wu has also advanced continual semantic mapping for city-scale environments, developing a three-layer sampling and panoptic representation method (10 citations) that allows robots to build and update dense maps over time. His research extends to multi-robot task allocation, where he leverages graph attention networks and unsupervised learning to solve large-scale coordination problems, and to sustainable urban mobility systems, reviewing safety and energy efficiency in collaborative intelligent agent ecosystems. More recently, Wu introduced DualMap, an online open-vocabulary mapping system that enables robots to navigate dynamic scenes through natural language queries. With a growing citation record and contributions spanning from foundational NeRF-based localization to practical multi-agent systems, Wu is shaping the future of autonomous navigation in complex, real-world environments.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5