Shoubin Chen

Shenzhen University, Peng Cheng Laboratory

Papers

Total Citations

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About

Shoubin Chen is a leading researcher at the forefront of robotics perception and multi-agent systems, with a primary focus on simultaneous localization and mapping (SLAM), sensor fusion, and intelligent tactile sensing. His most impactful work, a comparative analysis of SLAM algorithms for mechanical versus solid-state LiDAR (44 citations), has become a foundational reference for researchers navigating the rapidly evolving landscape of low-cost 3D sensing. Chen’s contributions extend to distributed multirobot SLAM, where he developed a robust, communication-efficient framework that fuses real-time intersection and historical loop constraints—a critical advancement for collaborative autonomy in unknown environments. Demonstrating remarkable versatility, he also pioneered a stretchable tactile sensor integrated with deep learning for 3D force decoding, enabling unprecedented haptic feedback for human-robot interfaces. This interdisciplinary work, bridging hardware innovation with algorithmic intelligence, has earned him recognition for pushing the boundaries of both environmental mapping and physical interaction. Chen’s research not only advances fundamental robotics but also provides practical solutions for real-world deployment, making him a key figure in the next generation of autonomous systems.