Papers
5
Total Citations
79
H-Index
3
About
Hongle Xie is a leading researcher in the field of long-term visual simultaneous localization and mapping (SLAM) for mobile robotics, with a particular focus on robust performance in dynamic and changing environments. His major contributions center on developing novel algorithms that enable robots to maintain accurate localization over extended periods, even when faced with significant scene changes. His most-cited work (2023, 45 citations) introduces a Bayesian persistence filter-based global map prediction method, directly addressing the degradation of localization accuracy in long-term SLAM. Xie further advanced the field with his work on robust incremental long-term visual topological localization (2022, 15 citations), which tackles the challenge of dynamic scene changes that cause traditional methods to fail. He has also developed a hierarchical forest-based fast online loop closure technique for low-latency consistent visual-inertial SLAM (2022, 14 citations). By moving beyond the static world assumption and incorporating online learning for feature existence state prediction, Xie’s research provides a critical foundation for deploying autonomous robots in real-world, long-term applications.
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