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About

Xiyue Guo is an emerging robotics researcher specializing in multi-robot systems, simultaneous localization and mapping (SLAM), and deep reinforcement learning. His work addresses some of the most pressing challenges in autonomous robotics, particularly enabling multiple robots to navigate and localize efficiently in large-scale, real-world environments. Guo's most influential contribution, "Semantic Histogram Based Graph Matching for Real-Time Multi-Robot Global Localization in Large Scale Environment" (2021), has garnered 78 citations, demonstrating significant community recognition for its novel approach to tackling viewpoint variation and computational efficiency in multi-robot SLAM. This work laid a strong foundation for his subsequent research, including "Descriptor Distillation for Efficient Multi-Robot SLAM," which advances bandwidth-conscious localization through compact feature descriptors, and cross-view semantic matching leveraging satellite imagery for ground-level robot localization. Beyond SLAM, Guo has broadened his research scope to multi-agent deep reinforcement learning, developing MultiRoboLearn, an open-source framework designed to bridge theoretical algorithms with practical multi-robot deployments. His work on single-line LiDAR depth completion further highlights his commitment to accessible, hardware-practical solutions for robot navigation. Collectively, Guo's research reflects a coherent vision: making collaborative autonomous robots more accurate, efficient, and deployable in demanding real-world scenarios.