ABSTRACT In this paper, a new mobile robot mapping algorithm inspired from the functionality of hippocampus cells is presented. Place cells in hippocampus can store a map of the environment. This model fuses odometry and vision data based on dimensionality reduction technique, hierarchically. These two types of data are first fused and then considered as inputs to the place cell model. Place cells do the clustering of places. The proposed Place cell model has two types of inputs: Grid cells input and input from the lateral entorhinal cortex (LEC). The LEC is modelled based on the dimension reduction technique. Therefore, the data that causes locations different to be inserted into the place cell from this layer. Another contribution is proposing a new unsupervised dimension reduction method based on k-means. The method can find perpendicular independent dimensions. Also, the distance of cluster centres found in these dimensions is maximised. The method was compared with LDA and PCA in standard functions. Although LDA is a supervised method, the result showed that the proposed unsupervised method outperformed. To evaluate the place cells model, sequences of images collected by a mobile robot was used and similar results to real place cells achieved.

中文翻译：

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基于降维技术的海马位置细胞功能启发的移动机器人映射模型

摘要 在本文中，提出了一种受海马细胞功能启发的新型移动机器人映射算法。海马体中的放置细胞可以存储环境地图。该模型基于降维技术分层融合里程计和视觉数据。这两种类型的数据首先被融合，然后被视为位置单元模型的输入。地点单元对地点进行聚类。建议的 Place 细胞模型有两种类型的输入：网格细胞输入和来自外侧内嗅皮层 (LEC) 的输入。LEC 是基于降维技术建模的。因此，导致位置不同的数据从该层插入到位置单元格中。另一个贡献是提出了一种新的基于 k-means 的无监督降维方法。该方法可以找到垂直的独立维度。此外，在这些维度中找到的聚类中心的距离最大化。该方法在标准函数中与 LDA 和 PCA 进行了比较。尽管 LDA 是一种有监督的方法，但结果表明，所提出的无监督方法的表现优于其他方法。为了评估位置单元模型，使用了移动机器人收集的图像序列，并获得了与真实位置单元相似的结果。