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About

Yunping Chen is a multidisciplinary researcher whose work spans two distinct but equally impactful domains: LiDAR-based autonomous navigation and satellite-derived aerosol remote sensing. In the field of robotics and autonomous systems, Chen's most-cited contribution, E-LOAM (2022, 55 citations), advances real-time LiDAR odometry and mapping by incorporating expanded local structural information, significantly improving performance in challenging unstructured environments — a critical step forward for autonomous vehicles and mobile robotics. Chen's broader body of work demonstrates a sustained commitment to high-resolution aerosol optical depth (AOD) retrieval over complex urban landscapes using Sentinel-2 satellite imagery. Recognizing the limitations of coarse-resolution aerosol products for air quality monitoring, Chen has developed innovative algorithms capable of producing 60-meter resolution AOD maps — a substantial improvement over conventional approaches. This research, supported by multi-sensor synergies including MODIS and Feng Yun-3C data, has garnered growing attention, with the 2021 high-resolution retrieval study accumulating 21 citations. Collectively, Chen's publications reflect a researcher driven by practical environmental and technological challenges, bridging remote sensing science and intelligent systems to deliver solutions with real-world relevance for urban monitoring and autonomous navigation alike.