Ziyu Pan

Papers

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About

Ziyu Pan is a researcher advancing the frontier of autonomous navigation in complex, unstructured environments. His work centers on two critical areas: semantic 3D mapping for urban driving and robust terrain perception for heavy industrial vehicles. In his highly cited 2019 paper, "Monocular Outdoor Semantic Mapping with a Multi-task Network," Pan pioneered a method to simultaneously extract geometric structure and semantic information from a single camera—a breakthrough that enables robots and autonomous cars to build rich, layered environmental models without expensive sensor suites. This work has garnered 8 citations and laid a foundation for efficient scene understanding. More recently, Pan tackled the extreme conditions of surface mining. His 2022 study, "Terrain Mapping for Autonomous Trucks in Surface Mine," introduces an extensible LiDAR-based mapping system that fuses point cloud, 2.5D grid, and mesh maps to handle rugged, dynamic terrain. With 7 citations, this research directly addresses the safety and reliability challenges of deploying autonomous trucks in hazardous industrial zones. By bridging the gap between academic mapping theory and real-world off-road deployment, Pan’s contributions are shaping the next generation of resilient, perception-driven autonomous systems.