About

Guolai Jiang is a leading researcher in mobile robotics, specializing in simultaneous localization and mapping (SLAM), sensor fusion, and autonomous navigation for service and surveillance robots. His most impactful work, cited 72 times, introduces a SLAM framework that fuses low-cost LiDAR with vision to build 2.5D maps, addressing the critical challenge of error accumulation in price-sensitive consumer robots. Jiang further advanced low-cost SLAM with an FFT-based scan-matching method (27 citations), significantly improving accuracy for robots using noisy laser range finders. His research extends to practical indoor navigation, including door detection and crossing using Kinect depth images (17 citations), enabling large surveillance robots to autonomously patrol complex environments. Jiang has also contributed to dynamic environment perception with a semantic geometric fusion multi-object tracking system (2024) and developed a lightweight multimodal person re-identification metric for person-following robots (2023). With over 180 total citations across his publications, Jiang’s work bridges the gap between theoretical SLAM algorithms and real-world deployment, making autonomous robots more accessible, reliable, and capable in everyday settings like restaurants, homes, and surveillance routes.

Research Focus

Key Achievements

7
H-Index
12
Papers
182
Total Citations
15
Avg Citations/Paper
🏆 Most Cited Paper
A Simultaneous Localization and Mapping (SLAM) Framework for 2.5D Map Building Based on Low-Cost LiDAR and Vision Fusion
72 citations · 2019
📈 Most Prolific Year: 2014 (3 Papers)
🤝 Key Collaborators: 34
🏛 Institutions: Chinese Academy of Sciences, Shenzhen Institutes of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen Academy of Robotics

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago