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About

Jingao Xu is a leading researcher in edge-assisted visual SLAM (Simultaneous Localization and Mapping), multi-agent robotic systems, and sensor fusion for autonomous navigation. Their seminal work, "Edge Assisted Mobile Semantic Visual SLAM" (2020), has garnered 91 citations and pioneered the offloading of computationally intensive SLAM tasks to edge servers, enabling real-time, resource-constrained applications like autonomous driving and inventory automation. Xu further advanced the field by addressing scalability in multi-agent systems, as demonstrated in "Scaling Up Edge-Assisted Real-Time Collaborative Visual SLAM Applications" (2023), which tackles coordination challenges for large-scale deployments. Their innovative fusion of camera and radar data for depth estimation, introduced in "Motion inspires notion" (2022) and expanded in "LeoVR" (2023), has set new benchmarks for accuracy in environmental perception. Notably, Xu’s recent work on high-speed object tracking with event cameras ("EventTracker," 2024) and wildfire suppression via multi-UAV scheduling ("FireHunter," 2024) showcases their commitment to solving real-world, time-critical problems. With over 150 total citations and a growing portfolio of impactful publications, Xu’s contributions are shaping the future of autonomous systems, from robotics to disaster response.