Cognitive Map Building Method Based on Rat Hippocampus Using CNN
YU Naigong1,2, WEI Yaqian1,2,3, WANG Lin1,2
1. Faculty of Information Technology, Beijing University of Technology, Beijing 100124; 2. Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing 100124; 3. Digital Community Ministry of Education Engineering Research Center, Beijing University of Technology, Beijing 100124
Abstract Rat hippocampal formation model fusing visual information has problems of low pose estimation accuracy of closed loop detection and inaccurate map construction. Aiming at the problems, a cognitive map building method based on rat hippocampal formation using convolutional neural network(CNN) is proposed. The improved CNN is utilized to extract visual input features. Location information is obtained by integrating spatial cell computing model and the information is fused to construct cognitive map. Hamming distance is adopted to calculate the similarity between visual information and images in visual library, and thus the familiar scene in the complex dynamic environment is recognized, and the self-positioning and position correction of the robot are completed. Simulation and physical experiments indicate that the proposed method is effective and robust.
Fund:Supported by National Natural Science Foundation of China(No.61573029), Beijing Municipal Natural Science Foundation(No.4162012)
Corresponding Authors:
YU Naigong, Ph.D., professor. His research interests include computational intelligence, intelligent system, robotics and robot technology, machine vision.
About author:: WEI Yaqian, master student. Her research interests include pattern recognition, intelligent system and bionic robot navigation.WANG Lin, master student. His research interests include pattern recognition, intelligent system and bionic robot navigation.
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2020, Vol. 33 
