基于卷积神经网络的仿鼠脑海马结构认知地图构建方法

Cognitive Map Building Method Based on Rat Hippocampus Using CNN

针对融合视觉信息的仿鼠脑海马模型闭环检测精度较低、地图构建不准确的问题,文中提出基于卷积神经网络的仿鼠脑海马结构认知地图构建方法.利用改进的卷积神经网络模型提取视觉输入特征,融合空间细胞计算模型得到位置信息,并构建认知地图.基于汉明距离计算视觉信息与视图库中图像的相似度,实现对复杂动态环境中熟悉场景的识别,完成机器人在环境中的定位及位置纠正.仿真与物理实验验证文中方法的有效性与鲁棒性.

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.