Papers
28
Total Citations
518
H-Index
10
About
Zonghai Chen is a leading researcher in autonomous robotics, specializing in Simultaneous Localization and Mapping (SLAM), mobile robot navigation, and deep learning for perception systems. His work bridges the critical gap between theoretical algorithms and real-world deployment in dynamic, unstructured environments. Chen’s most impactful contribution is **DP-SLAM** (151 citations), a visual SLAM system that introduces a moving probability model to robustly handle dynamic objects—a persistent challenge in autonomous navigation. He has also advanced particle filter localization by integrating particle swarm optimization (119 citations), significantly improving accuracy for mobile robots. Beyond SLAM, Chen has pioneered deep learning approaches for indoor navigation, including a CNN-based system that achieves reliable visual localization with minimal training data (35 citations). His recent work on LiDAR SLAM introduces geometry feature group-based stable feature selection and three-stage loop closure optimization (26 citations), enhancing long-term autonomy for ground robots. With over 400 total citations across his portfolio, Chen’s research consistently addresses practical limitations—from dynamic environments to data scarcity—making his methods highly adoptable in both academic and industrial robotics applications.
Research Focus
Key Achievements
Top Papers
- 1DP-SLAM: A visual SLAM with moving probability towards dynamic environments151 citations · 2021
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- 9A Novel Hybrid Map Based Global Path Planning Method15 citations · 2018
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