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| Mobile Robot SLAM Method Inspired by Cognitive Mechanisms of Visual Cortex and Entorhinal-Hippocampal System |
| LIAO Yishen1, YU Naigong2, YU Hejie3, WANG Chenghua1 |
1. School of Virtual Reality and Modern Industry, Jiangxi University of Finance and Economics, Nanchang 330032;
2. School of Information Science and Technology, Beijing University of Technology, Beijing 100124; 3. Department of Precision Instrument, Tsinghua University, Beijing 100084 |
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Abstract To address the problem of drift accumulation and insufficient loop-closure robustness of traditional SLAM methods in large-scale, dynamic and visually degraded scenes, a mobile robot SLAM method inspired by cognitive mechanisms of visual cortex and entorhinal-hippocampal system(VEH-SLAM) is proposed. Based on the pose estimation of entorhinal-hippocampal path integration model, VEH-SLAM is designed to construct a view-cell-inspired episodic memory network. Maximum a posteriori estimation and a posterior entropy-driven adaptive threshold mechanism are utilized to significantly improve the accuracy and robustness of loop-closure detection. In addition, an efficient cognitive map learning algorithm is developed based on the basic map learning module. Spatial-density pruning and neural-scale stretching are integrated into the algorithm. The map size is effectively controlled, and the encoding resolution is adaptively adjusted. Therefore, mapping speed and accuracy are balanced. VEH-SLAM is shown to achieve the highest recall at 100% precision on five of the seven public datasets. In terms of mapping, the total time is significantly reduced, and the lowest map errors are obtained on some sequences. Real-time performance and engineering feasibility are further verified through indoor and outdoor physical experiments.
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Received: 01 June 2026
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| Fund:Supported by National Natural Science Foundation of China(No.62566024,62076014), Science and Technology Research Project of Department of Education of Jiangxi Province(No.GJJ2400402), Early-Career Young Scientists and Technologists Project of Jiangxi Province(No.20252BEJ730116) |
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Corresponding Authors:
YU Naigong, Ph.D., professor. His research interests include pa-ttern recognition, brain-inspired computing, and robot environment perception.
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About author:: LIAO Yishen, Ph.D., lecturer. His research interests include brain-inspired computing, bionic modeling, and robot navigation.
YU Hejie, Ph.D., engineer. His research interests include robot navigation, intelligent systems, and vehicle-cloud collaboration.
WANG Chenghua, Master student. His research interests include artificial intelligence, brain-inspired computing, and bionic modeling. |
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2026, Vol. 39 
