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About

Yumeng Xiu is an emerging robotics researcher whose work sits at the intersection of autonomous navigation, dynamic obstacle perception, and unmanned aerial vehicle (UAV) systems. Specializing in real-time environmental understanding for mobile robots, Xiu has made significant contributions to how autonomous systems perceive and respond to dynamic, cluttered environments using lightweight RGB-D camera setups — a practical alternative to heavy LiDAR-based solutions common in autonomous driving. Among Xiu's most impactful contributions is a suite of interconnected systems enabling robots to detect, track, and avoid moving obstacles in real time. Their 2023 work on onboard dynamic-object detection for autonomous robots has already garnered 57 citations, reflecting its strong relevance to the robotics community. Complementing this, Xiu developed a gradient-based B-spline trajectory optimization framework for UAV navigation in dynamic environments (40 citations), and a robust dynamic obstacle tracking and mapping system tailored for UAVs (35 citations). A particularly notable application of this research is an autonomous UAV inspection framework for hazardous tunnel construction sites, demonstrating real-world deployment under challenging conditions. Collectively, Xiu's work represents a coherent and rapidly influential research agenda advancing safe, vision-based autonomous navigation.