Before the cognitive map is generated through the fire of the rodent hippocampal spatial cells, mammals can obtain the outside knowledge through the visual information, which comes from the eyeball to the brain. The information is encoded and transferred to the two regions of the brain based on the fact of biophysiological research, which are known as “what” loop and “where” loop. In this article, we simulate an episodic memory recognition unit consisting of the integration of two-loop information, which is applied to building the accurate bioinspired spatial cognitive map of real environments. We employ the visual bag of word algorithm based on oriented Feature from Accelerated Segment Test and rotated Binary Robust Independent Elementary Features feature to build the “what” loop and the hippocampal spatial cells cognitive model, which comes from the front-end visual information input system to build the “where” loop. At the same time, the environmental cognitive map is a topological map containing information about place cell competition firing rate, oriented Feature from Accelerated Segment Test and rotated Binary Robust Independent Elementary Features feature descriptor, similarity of image retrieval, and relative location of cognitive map nodes. The simulation experiments and physical experiments in a mobile robot platform have been done to verify the environmental adaptability and robustness of the algorithm. This proposing algorithm would provide a foundation for further research on bioinspired navigation of robots.

中文翻译：

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基于情景记忆识别的改进仿生认知地图构建系统

在通过啮齿动物海马空间细胞的燃烧生成认知图之前，哺乳动物可以通过视觉信息获取外界的知识，视觉信息从眼球传到大脑。根据生物生理研究的事实，信息被编码并传输到大脑的两个区域，这两个区域被称为“什么”环和“哪里”环。在本文中，我们模拟了一个由两环信息集成组成的情景记忆识别单元，用于构建真实环境的精确仿生空间认知图。我们采用基于加速段测试的定向特征和旋转二进制鲁棒独立基本特征特征的词视觉袋算法来构建“什么”循环和海马空间细胞认知模型，来自前端视觉信息输入系统构建“where”循环。同时，环境认知图是一个拓扑图，包含位置细胞竞争放电率、来自加速段测试的定向特征和旋转二元鲁棒独立基本特征特征描述符、图像检索的相似性、认知图节点的相对位置等信息。 . 在移动机器人平台上进行了仿真实验和物理实验，验证了算法的环境适应性和鲁棒性。该算法将为进一步研究仿生机器人导航奠定基础。环境认知图是一个拓扑图，包含位置细胞竞争放电率、来自加速段测试的定向特征和旋转二元鲁棒独立基本特征特征描述符、图像检索的相似性和认知图节点的相对位置等信息。在移动机器人平台上进行了仿真实验和物理实验，验证了算法的环境适应性和鲁棒性。该算法将为进一步研究仿生机器人导航奠定基础。环境认知图是一个拓扑图，包含位置细胞竞争放电率、来自加速段测试的定向特征和旋转二元鲁棒独立基本特征特征描述符、图像检索的相似性和认知图节点的相对位置等信息。在移动机器人平台上进行了仿真实验和物理实验，验证了算法的环境适应性和鲁棒性。该算法将为进一步研究仿生机器人导航奠定基础。在移动机器人平台上进行了仿真实验和物理实验，验证了算法的环境适应性和鲁棒性。该算法将为进一步研究仿生机器人导航奠定基础。在移动机器人平台上进行了仿真实验和物理实验，验证了算法的环境适应性和鲁棒性。该算法将为进一步研究仿生机器人导航奠定基础。