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About

Hong Xie is a leading researcher in robotic perception and autonomous navigation, with a core focus on Simultaneous Localization and Mapping (SLAM) in dynamic and unstructured environments. His major contributions lie in developing robust visual and LiDAR-inertial SLAM systems that break free from the traditional static-world assumption, enabling robots to operate reliably in real-world scenes with moving objects. His highly cited 2022 work, "DGS-SLAM," integrates geometric and semantic information to achieve fast, robust RGBD SLAM in dynamic environments, garnering 72 citations for its practical impact. Xie has also advanced hierarchical multiview registration for terrestrial laser scanning point clouds and pioneered LiDAR-inertial odometry with moving object detection, addressing critical challenges in dynamic scenes. His recent 2025 work on hybrid frontier-guided autonomous exploration for UAVs further demonstrates his commitment to enabling complete, high-quality mapping in unknown environments. With a publication record that includes top-tier venues and a growing citation impact, Hong Xie is recognized for bridging the gap between SLAM theory and real-world robotic autonomy, making his research essential reading for students and engineers working on perception for mobile robots, autonomous vehicles, and field robotics.