Wenlu Qi

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About

Wenlu Qi is a researcher advancing the frontiers of visual simultaneous localization and mapping (vSLAM) for autonomous robotics, with a particular focus on overcoming real-world environmental challenges. Her work addresses two critical limitations in current vSLAM systems: robustness under dynamic lighting conditions and stability in high-motion indoor settings. In her highly cited 2024 study on adaptive lighting vSLAM with limited on-device resources (37 citations), Qi developed novel methods to maintain precise robot localization despite sudden illumination changes, reflections, and shadows—a persistent problem for indoor mobile robots. Building on this foundation, her 2025 work on optimized feature points and keyframe methods tackles the equally difficult challenge of high-dynamic environments where moving objects and occlusions disrupt traditional static-environment algorithms. By engineering more resilient feature extraction and keyframe selection strategies, Qi is enabling robots to achieve reliable self-awareness in the unpredictable, real-world spaces where they must ultimately operate. Her contributions are directly relevant to the deployment of service robots, autonomous vehicles, and industrial automation systems that demand consistent performance under non-ideal conditions.