Papers
2
Total Citations
21
H-Index
2
About
Zejie Lv is an emerging researcher specializing in computer vision and autonomous systems, with a particular focus on Simultaneous Localization and Mapping (SLAM) for intelligent transportation and robotics applications. His most notable contribution, **Light-SLAM**, represents a significant advancement in visual SLAM technology by integrating deep learning — specifically the LightGlue feature matching framework — to overcome the persistent limitations of traditional hand-crafted feature-based methods in challenging lighting conditions. This work directly addresses one of the most critical bottlenecks in real-world autonomous driving and robotic navigation, where variable illumination frequently degrades localization reliability and mapping accuracy. Published across 2024 and 2025, Light-SLAM has already accumulated 21 combined citations, with the 2025 version alone drawing 19 citations — a remarkable early-stage impact that signals strong community interest in deep-learning-enhanced SLAM pipelines. Lv's research sits at the intersection of robotics, autonomous driving, and applied deep learning, positioning him as a promising contributor to the next generation of robust perception systems. Students and researchers working on real-world deployment of autonomous agents will find his work particularly relevant to bridging the gap between controlled-environment performance and reliable outdoor operation.
Research Focus
Key Achievements
Top Papers
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