Peipei Fan

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About

Peipei Fan is a robotics researcher whose work focuses on advancing simultaneous localization and mapping (SLAM) for mobile robots operating in complex, dynamic environments. Her key research areas include semantic perception, 3D LiDAR odometry, and robust mapping under challenging conditions. Fan’s most notable contribution is the development of semantic LiDAR odometry and mapping that integrates RangeNet++ to filter dynamic objects, addressing a critical limitation of traditional SLAM systems that assume static surroundings. This work, published in 2022, has already garnered 4 citations, signaling its growing influence in the field. By enabling robots to distinguish between static and moving elements in real time, Fan’s approach significantly improves registration accuracy and system reliability in highly dynamic settings—such as crowded urban areas or busy indoor spaces. Her research bridges the gap between deep learning-based semantic segmentation and geometric mapping, offering a practical solution for autonomous navigation. As a rising scholar, Fan’s work holds promise for applications in autonomous driving, service robotics, and industrial automation, where adaptability to changing environments is essential.