Papers
1
Total Citations
45
H-Index
1
About
Li Wan is a leading researcher in mobile robotics, specializing in LiDAR-based perception, localization, and mapping. His most influential work tackles the fundamental challenge of efficient and reliable global localization in large-scale environments. In his highly cited 2021 paper, Wan introduced an optimized branch-and-bound (BnB) algorithm that leverages multiscale and multiresolution maps to dramatically reduce the search space for robot pose estimation. This innovation enables mobile robots to determine their position globally without prior knowledge, even in complex, structured scenes, overcoming a critical bottleneck in autonomous navigation. With 45 citations on this paper alone, Wan’s contributions have directly advanced the robustness and speed of LiDAR-based systems, influencing both academic research and real-world deployment in logistics, warehouse automation, and service robotics. His work is widely recognized for bridging the gap between theoretical optimization and practical, real-time performance. For students and researchers, Wan’s research offers a clear path into the intersection of sensor fusion, computational geometry, and autonomous systems—a must-read for anyone working on robot self-localization in GPS-denied or dynamic environments.
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Top Papers
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