Roboticists have long drawn inspiration from nature to develop navigation and simultaneous localization and mapping (SLAM) systems such as RatSLAM. Animals such as birds and bats possess superlative navigation capabilities, robustly navigating over large, three-dimensional environments, leveraging an internal neural representation of space combined with external sensory cues and self-motion cues. This paper presents a novel neuro-inspired 4DoF (degrees of freedom) SLAM system named NeuroSLAM, based upon computational models of 3D grid cells and multilayered head direction cells, integrated with a vision system that provides external visual cues and self-motion cues. NeuroSLAM's neural network activity drives the creation of a multilayered graphical experience map in a real time, enabling relocalization and loop closure through sequences of familiar local visual cues. A multilayered experience map relaxation algorithm is used to correct cumulative errors in path integration after loop closure. Using both synthetic and real-world datasets comprising complex, multilayered indoor and outdoor environments, we demonstrate NeuroSLAM consistently producing topologically correct three-dimensional maps.

中文翻译：

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NeuroSLAM：用于3D环境的灵感来自大脑的SLAM系统。

长期以来，机器人专家一直从自然界中汲取灵感，以开发导航以及同步定位和地图绘制（SLAM）系统，例如RatSLAM。鸟类和蝙蝠等动物具有最高级的导航功能，可以在空间的内部三维表示中结合外部感官提示和自我运动提示，从而在大型三维环境中稳健地导航。本文基于3D网格单元和多层头部方向单元的计算模型，结合提供外部视觉提示和自我运动提示的视觉系统，提出了一种新型的神经启发性4DoF（自由度）SLAM系统，名为NeuroSLAM。NeuroSLAM的神经网络活动可实时创建多层图形体验图，通过熟悉的本地视觉提示序列实现重新定位和循环闭合。多层经验图松弛算法用于校正闭环后路径积分中的累积误差。使用包含复杂的多层室内和室外环境的合成数据和真实数据集，我们证明NeuroSLAM始终可生成拓扑正确的三维地图。