Jinge Tu
Papers
2
Total Citations
10
H-Index
2
About
Jinge Tu is a researcher specializing in robotics, autonomous navigation, and indoor localization systems, with a particular focus on Simultaneous Localization and Mapping (SLAM) technologies. Their most recognized work centers on laser-based SLAM methodologies designed to address the complex challenges posed by dynamic indoor environments — a critical problem for real-world deployment of autonomous systems and Location-Based Services (LBS). Tu's notable contribution, "Laser-Based SLAM with Efficient Occupancy Likelihood Map Learning for Dynamic Indoor Scenes" (2016), introduces an efficient approach to map building in unknown, changing environments using laser sensing combined with occupancy likelihood map learning. This work tackles one of the fundamental limitations of traditional SLAM systems — their reduced reliability when environments are not static — making it highly relevant to practical applications in robotics, indoor navigation, and smart building technologies. The paper has accumulated citations across multiple publication venues, reflecting its relevance to both the robotics and geospatial research communities. Tu's research sits at the intersection of mobile robotics, sensor fusion, and spatial intelligence, contributing meaningful advances to how autonomous agents perceive and map their surroundings. Their work provides a valuable foundation for students and researchers exploring real-time indoor mapping and intelligent navigation systems.
Research Focus
Key Achievements
Top Papers
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- 2