Spatial navigation depends on the combination of multiple sensory cues from idiothetic and allothetic sources. The computational mechanisms of mammalian brains in integrating different sensory modalities under uncertainty for navigation is enlightening for robot navigation. We propose a Bayesian attractor network model to integrate visual and vestibular inputs inspired by the spatial memory systems of mammalian brains. In the model, the pose of the robot is encoded separately by two sub-networks, namely head direction network for angle representation and grid cell network for position representation, using similar neural codes of head direction cells and grid cells observed in mammalian brains. The neural codes in each of the sub-networks are updated in a Bayesian manner by a population of integrator cells for vestibular cue integration, as well as a population of calibration cells for visual cue calibration. The conflict between vestibular cue and visual cue is resolved by the competitive dynamics between the two populations. The model, implemented on a monocular visual simultaneous localization and mapping (SLAM) system, termed NeuroBayesSLAM, successfully builds semi-metric topological maps and self-localizes in outdoor and indoor environments of difference characteristics, achieving comparable performance as previous neurobiologically inspired navigation systems but with much less computation complexity. The proposed multisensory integration method constitutes a concise yet robust and biologically plausible method for robot navigation in large environments. The model provides a viable Bayesian mechanism for multisensory integration that may pertain to other neural subsystems beyond spatial cognition.

中文翻译：

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NeuroBayesSLAM：受神经生物学启发的贝叶斯多感官信息集成，用于机器人导航。

空间导航取决于惯用和惯用来源的多种感觉线索的组合。在不确定的导航条件下整合不同的感觉模态的哺乳动物大脑的计算机制对于机器人导航具有启发性。我们提出了贝叶斯吸引网络模型，以整合受到哺乳动物大脑空间记忆系统启发的视觉和前庭输入。在模型中，机器人的姿态由两个子网络分别编码，即用于角度表示的头部方向网络和用于位置表示的网格细胞网络，使用在哺乳动物大脑中观察到的头部细胞和网格细胞的类似神经代码。每个子网络中的神经代码都通过一组用于前庭提示整合的整合细胞以贝叶斯方式进行更新，以及用于视觉提示校准的校准单元。前庭提示和视觉提示之间的冲突由两个人群之间的竞争动力学解决。该模型在名为NeuroBayesSLAM的单眼视觉同时定位和制图（SLAM）系统上实施，成功建立了半度量拓扑图，并在具有不同特征的室外和室内环境中进行了自定位，可实现与以前的受神经生物学启发的导航系统相当的性能，但是计算复杂度大大降低。所提出的多传感器集成方法构成了在大型环境中进行机器人导航的简洁而健壮且生物学上可行的方法。