Papers
2
Total Citations
66
H-Index
2
About
Dewen Wu is a researcher whose work sits at the intersection of computer vision, robotics, and location-based services. His primary research areas include indoor visual positioning and physical human-robot interaction, with a focus on developing practical, training-free solutions for real-world applications. Wu’s most notable contribution is his 2018 paper, "Indoor Visual Positioning Aided by CNN-Based Image Retrieval: Training-Free, 3D Modeling-Free," which has garnered 60 citations. This work addresses a critical challenge in location-based services—seamless indoor navigation—by leveraging convolutional neural networks for image retrieval without the need for extensive pre-training or 3D modeling. The approach has implications for precision marketing, robotics spatial cognition, and autonomous navigation. In addition, Wu has explored external force detection for physical human-robot interaction, as seen in his 2017 paper (6 citations), where he used dynamic model identification to enhance robot safety and responsiveness. His research bridges theoretical advances with deployable systems, making him a key figure in advancing practical indoor localization and collaborative robotics.
Research Focus
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Top Papers
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