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About

Dan Jia is a robotics researcher whose work sits at the intersection of mobile robot perception, autonomous navigation, and human detection. His research focuses primarily on enabling robots to reliably detect and interact with people in real-world environments, with a particular emphasis on LiDAR-based sensing systems and deep learning methodologies. Among his most recognized contributions is a comparative study of 2D versus 3D LiDAR-based person detection on mobile robots (2022, 17 citations), which has become a valuable reference for researchers designing perception pipelines for human-populated spaces. Complementing this, his work on self-supervised person detection in 2D range data (2021, 12 citations) addresses a critical bottleneck in the field — the scarcity of annotated training datasets — by leveraging calibrated cameras to generate supervision automatically. His investigation into domain and modality gaps in LiDAR-based detection further highlights the practical challenges of deploying these systems across diverse environments and sensor configurations. Earlier in his career, Jia contributed to autonomous exploration through a novel coverage path planning algorithm for legged robots in unknown environments (2016, 16 citations), demonstrating a broad command of robot autonomy. Together, his publications reflect a consistent commitment to making mobile robots safer and more capable in complex, human-centered settings.