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About

Xingguang Zhong is a leading robotics researcher whose work lies at the intersection of autonomous navigation, multi-robot coordination, and active perception. He is best known for developing PIN-SLAM (2024, 68 citations), a pioneering LiDAR SLAM system that leverages point-based implicit neural representations to achieve globally consistent maps—a breakthrough for long-term autonomous operation. Zhong also introduced the Meeting-Merging-Mission framework (2022, 60 citations), a complete multi-robot exploration system designed for communication-limited environments, addressing real-world constraints of bandwidth and range. His research extends to visibility planning, where he proposed star-convex constrained optimization for aerial inspection tasks, and to active scene reconstruction with ActiveGS (2025), which uses Gaussian splatting for efficient map building. Notably, Zhong has also ventured into agricultural robotics, developing zero-shot semantic segmentation methods to enable robots to identify weeds without task-specific training. His work has accumulated over 140 citations, reflecting its significant impact on both theoretical foundations and practical applications in robotics. Zhong’s contributions are essential reading for researchers interested in SLAM, exploration, and perception for autonomous systems.