Wenbang Deng

National University of Defense Technology

Papers

Total Citations

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About

Wenbang Deng is a leading researcher in robotics and autonomous systems, with a primary focus on semantic simultaneous localization and mapping (SLAM) and point cloud registration. His most impactful work, the 2020 paper "Semantic RGB-D SLAM for Rescue Robot Navigation" (30 citations), introduces a novel framework that integrates convolutional neural networks with RGB-D SLAM to generate dense, semantically labeled point-cloud maps. This innovation enables rescue robots to not only navigate hazardous environments but also understand their surroundings at a semantic level—a critical capability for disaster response. Deng further advanced autonomous driving technology with his 2023 paper "RDMNet: Reliable Dense Matching Based Point Cloud Registration for Autonomous Driving" (27 citations), which addresses the challenge of accurate ego-motion estimation through a coarse-to-fine matching approach that outperforms existing methods reliant on superpoint correspondences. His contributions bridge the gap between geometric mapping and semantic understanding, earning recognition for their practical impact in both rescue robotics and autonomous driving. Deng’s work continues to shape how robots perceive and interact with complex, unstructured environments.