自动化学报2018，Vol.44Issue(1)：52-73,22.DOI:10.16383/j.aas.2018.c160467
一种基于海马认知机理的仿生机器人认知地图构建方法
A Cognitive Map Building Algorithm by Means of Cognitive Mechanism of Hippocampus
摘要
Abstract
Spatial cells in hippocampus play a functional role in representation and processing of spatial information, which appear to provide a basis for cognitive map: a representation of environment. Most prior biomimetic map building algorithms, such as RatSLAM algorithm or traditional SLAM methods, have little biological fidelity to the hippocampal formation. In this paper,a neural network model based on the behavioral and neurophysiological mechanisms of the spatial cells is constructed, and is applied to building the accurate cognitive map of real environments. The proposed algorithm has a uniform calculation method for spatial cells based on continuous attractor network dynamics to integrate self-motion cues, which can reproduce grid cells firing responses and place cells firing fields via feedforward inputs from band cells. RGB-D images serve as visual cues for loop closure detection and correcting the accumulative errors intrinsically associated with the path integration mechanism, which contributes to building spatial cognitive maps of indoor environments on a mobile robot. A cognitive map is a fine-grained topological-metric map. A node in the cognitive map is constructed by associating the major peak of place cell population activities with corresponding visual cues and the transition stores the change in positions. Simulation experiments and physical experiments with a mobile robot have verified the effectiveness of the algorithm. The proposed algorithm may provide a foundation for robotic navigation.关键词
认知地图/海马结构/空间细胞/条纹细胞/闭环检测Key words
Cognitive map/hippocampal formation/spatial cells/band cells/loop closure detection引用本文复制引用
于乃功,苑云鹤,李倜,蒋晓军,罗子维..一种基于海马认知机理的仿生机器人认知地图构建方法[J].自动化学报,2018,44(1):52-73,22.基金项目
国家自然科学基金(61573029),北京市自然科学基金(4162012)资助Supported by National Natural Science Foundation of China(61573029)and Beijing Natural Science Foundation(4162012) （61573029）
