Shipeng Zhong
Papers
7
Total Citations
137
H-Index
6
About
Shipeng Zhong is a leading researcher in collaborative simultaneous localization and mapping (SLAM) for multi-robot systems, with a focus on enabling robotic swarms to operate autonomously in GPS-denied and unknown environments. His most influential contribution is **DCL-SLAM**, a distributed collaborative LiDAR SLAM framework that allows a robotic swarm to establish a shared global reference frame without any prior environmental knowledge—a foundational work with 58 citations. Zhong also created the **S3E dataset** (28 citations), the first multi-robot multimodal dataset designed specifically for collaborative SLAM research, addressing critical gaps in scalability and sensor diversity. His recent work, **CaRtGS** (12 citations), pushes the frontier of real-time photorealistic scene reconstruction by aligning computational efficiency with Gaussian Splatting SLAM. Additionally, **SCL-SLAM** (9 citations) integrates Scan Context loop closure into LiDAR-inertial odometry, while **CoLRIO** (8 citations) tackles centralized state estimation for heterogeneous sensor swarms. Zhong’s research directly addresses the core challenges of distributed perception and coordination, making him a key figure in advancing practical, scalable collaborative robotics.
Research Focus
Key Achievements
Top Papers
- 1
- 2S3E: A Multi-Robot Multimodal Dataset for Collaborative SLAM28 citations · 2024
- 3S3E: A Multi-Robot Multimodal Dataset for Collaborative SLAM17 citations · 2022
- 4CaRtGS: Computational Alignment for Real-Time Gaussian Splatting SLAM12 citations · 2025
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