Hui Cheng
Papers
52
Total Citations
1,152
H-Index
17
About
Hui Cheng is a prominent researcher whose work spans computer vision, simultaneous localization and mapping (SLAM), autonomous robotics, and multi-robot systems. His contributions have significantly advanced the intersection of deep learning and real-world robotic applications, with a particular focus on scene understanding, 3D reconstruction, and autonomous navigation in complex environments. Cheng's most influential work includes "LSTM-CF," which unified context modeling and fusion for RGB-D scene labeling using LSTMs, accumulating 188 citations and demonstrating his early mastery of deep learning for spatial perception. His "Photo-SLAM" system (151 citations) exemplifies his cutting-edge contributions to photorealistic, real-time mapping—a critical breakthrough for resource-constrained portable devices. In autonomous exploration, his FAEL framework (77 citations) tackled scalability challenges for mobile robots operating in large-scale environments, while his distributed LiDAR SLAM system advanced cooperative multi-robot mapping. Cheng has also made notable strides in 6D pose estimation for robotic manipulation, dynamic obstacle avoidance, UAV-based aerial grasping, and ground-aerial collaborative mapping. His deep reinforcement learning approach to decentralized multi-robot navigation further illustrates his breadth across robotics intelligence. With over 700 combined citations across his top works, Cheng represents a leading voice in intelligent, perception-driven autonomous systems.
Research Focus
Key Achievements
Top Papers
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- 5RDC-SLAM: A Real-Time Distributed Cooperative SLAM System Based on 3D LiDAR53 citations · 2021
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- 9Ground and Aerial Collaborative Mapping in Urban Environments36 citations · 2020
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