Yanfu Fan

Papers

Total Citations

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About

Yanfu Fan is a researcher in robotics and computer vision, with a primary focus on visual localization and 3D mapping for autonomous navigation. His most-cited work, "Visual Localization in a Prior 3D LiDAR Map Combining Points and Lines" (2021, 3 citations), addresses a critical challenge in vision-based navigation: the drift accumulation in visual and visual-inertial odometry (VO/VIO) systems when loop closures are absent. Fan’s key contribution lies in enhancing localization accuracy by fusing both point and line features from a prior 3D LiDAR map, moving beyond methods that rely solely on points—which suffer from low precision and scalability issues. This hybrid approach improves robustness in complex environments, offering a more reliable solution for long-term autonomous operations. While his citation count is modest, the work targets a fundamental problem in robotics, bridging the gap between visual sensors and LiDAR-based prior maps. Fan’s research is particularly relevant for applications in self-driving cars, drones, and mobile robots, where precise localization is essential. His contributions highlight the importance of multi-modal feature integration, paving the way for more resilient navigation systems in GPS-denied or dynamic settings.