Papers
4
Total Citations
48
H-Index
4
About
Jingwen Yu is a leading researcher in robotics and autonomous systems, with a primary focus on advancing Simultaneous Localization and Mapping (SLAM), visual place recognition, and semantic scene understanding for mobile and assistive robots. Their most impactful contribution is the development of **FusionPortableV2**, a unified multi-sensor dataset designed to generalize SLAM algorithms across diverse platforms and scalable environments—a critical step toward robust, real-world deployment. This work has already garnered 33 citations since its 2024 release, underscoring its immediate influence on the field. Yu also pioneered a **cloud-learning and edge-model hybrid framework** for VSLAM, enabling mobile robots to offload heavy computation without sacrificing real-time performance, and introduced a **convolutional autoencoder** for condition-invariant visual place description, achieving robust recognition under varying lighting and weather. In the domain of assistive robotics, Yu developed a **relationship-oriented semantic scene understanding** approach, allowing robots to interpret object interactions for daily manipulation tasks—a key enabler for disability support systems. With a growing citation record and a focus on bridging learning-based and model-based methods, Jingwen Yu is shaping the next generation of scalable, intelligent robotic navigation.
Research Focus
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Top Papers
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