Yukun Guo

Papers

Total Citations

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About

Yukun Guo is a researcher in mobile robotics and 3D laser scanning, with a focus on terrain-adaptive mapping and sensor fusion. His most cited work, "Map merging with terrain-adaptive density using mobile 3D laser scanner" (2020), addresses a critical challenge in autonomous navigation: generating consistent, high-resolution maps from multiple scanning sessions in uneven environments. By introducing a density-aware merging algorithm that adapts to terrain features, Guo’s approach improves map accuracy and reduces computational overhead, enabling more reliable localization for field robots. Although his citation count is still growing—with the 2020 paper accumulating 9 citations—this work has been recognized for its practical relevance in applications like agricultural robotics and disaster response. Guo’s contributions lie at the intersection of perception and mapping, where he develops methods that balance data density with real-time performance. His research is particularly valuable for students and engineers working on SLAM in unstructured outdoor settings, offering a scalable solution for merging point clouds from mobile scanners. As his publication record expands, Guo continues to advance the state of the art in terrain-adaptive 3D mapping.