Qiuyu Zang

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About

Qiuyu Zang is a robotics researcher whose work focuses on advancing visual simultaneous localization and mapping (SLAM) in challenging, real-world environments. Zang’s primary contributions target the critical problem of dynamic objects—such as moving pedestrians or vehicles—that degrade SLAM accuracy by corrupting epipolar geometry. In their highly cited 2023 paper “DN-SLAM: A Visual SLAM With ORB Features and NeRF Mapping in Dynamic Environments” (67 citations), Zang introduced a novel hybrid framework that integrates ORB feature-based localization with Neural Radiance Fields (NeRF) for dense mapping, achieving robust performance even in scenes with significant motion. This work directly addresses the long-standing tension between accurate pose estimation and consistent map building in dynamic settings. Building on this, Zang’s “An Adaptive ORB-SLAM3 System for Outdoor Dynamic Environments” (23 citations) extends the state-of-the-art ORB-SLAM3 pipeline with adaptive mechanisms tailored for outdoor conditions, where lighting changes and unpredictable object movements pose additional hurdles. Together, these contributions have earned Zang recognition for pushing SLAM systems toward practical deployment in autonomous navigation and field robotics. Their work is essential reading for researchers tackling the intersection of computer vision, mapping, and real-time robotic perception.