Jingxing Qian

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About

Jingxing Qian is a robotics researcher specializing in 3D mapping, scene understanding, and autonomous navigation in dynamic real-world environments. His work addresses one of the field's most pressing challenges: enabling robots to operate reliably over extended periods in environments that change over time — so-called semi-static scenes. Qian's most recognized contribution, "POCD: Probabilistic Object-Level Change Detection and Volumetric Mapping in Semi-Static Scenes" (2022, 14 citations), introduced a principled probabilistic framework for detecting object-level changes and maintaining accurate, up-to-date maps — a critical capability for long-term robot deployment. Building on this foundation, his 2024 work on uncertainty-aware 3D object-level mapping (4 citations) advances reconstruction of high-quality object maps without requiring CAD models, broadening applicability to unknown environments. His research on semantically safe navigation (3 citations) further closes the loop between perception and action, equipping autonomous robots with adaptive strategies to navigate safely under real-world uncertainty. Together, Qian's contributions span the full pipeline from environment perception to safe robot decision-making, reflecting a coherent research vision centered on robust, semantically rich autonomy. His growing citation record signals increasing recognition within the robotics and computer vision communities.