Jingwen Luo
Papers
7
Total Citations
67
H-Index
5
About
Jingwen Luo is a leading researcher in mobile robotics, specializing in Simultaneous Localization and Mapping (SLAM) for dynamic and semi-static environments. Her work addresses a critical challenge: enabling robots to accurately perceive and navigate spaces where moving objects—from pedestrians to vehicles—cause significant pose estimation errors. Luo’s major contributions include pioneering semantic visual SLAM systems that integrate deep learning for robust performance. Her 2023 paper “YES-SLAM” (14 citations) leverages YOLOv7 to filter dynamic objects, while the 2024 “YS-SLAM” (13 citations) uses YOLACT++ for adaptive environment handling. Earlier, her 2018 FastSLAM algorithm (24 citations) tackled particle degeneracy in probabilistic mapping. More recently, she has advanced 3D SLAM with point-line features and superpixel segmentation (2025, 7 citations), and introduced SPVL-vSLAM (2025, 2 citations) for autonomous driving in semi-static scenes. With over 67 total citations and a trajectory from foundational algorithms to cutting-edge deep-learning-enhanced systems, Luo’s research is shaping the next generation of autonomous navigation—making robots smarter, safer, and more reliable in the unpredictable real world.
Research Focus
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Top Papers
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