Jinbin Tan

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About

Jinbin Tan is a leading researcher in geospatial data science, with a primary focus on 3D mapping, LiDAR-based modeling, and cooperative sensing for indoor environments. His most cited work, "Cooperative indoor 3D mapping and modeling using LiDAR data" (2021, 28 citations), introduces a novel framework that leverages multiple LiDAR sensors to collaboratively reconstruct high-fidelity 3D models of complex indoor spaces. This contribution addresses critical challenges in autonomous navigation and digital twin creation, where accurate, real-time mapping is essential. Tan’s research bridges the gap between sensor fusion and spatial intelligence, enabling more robust and scalable mapping solutions for robotics, smart buildings, and infrastructure monitoring. His work has been recognized for its practical impact, particularly in improving the efficiency and accuracy of indoor localization systems. With a growing citation record, Tan continues to push the boundaries of cooperative perception and 3D modeling, making him a notable figure in the advancement of geospatial technology for indoor applications.