Yu Fangwen, Shang Jianga, Hu Youjian, Milford Michael
Faculty of Information Engineering, China University of Geosciences and National Engineering Research Center for Geographic Information System, Wuhan, 430074, China.
Science and Engineering Faculty, Queensland University of Technology and Australian Centre for Robotic Vision, Brisbane, QLD, 4000, Australia.
Biol Cybern. 2019 Dec;113(5-6):515-545. doi: 10.1007/s00422-019-00806-9. Epub 2019 Sep 30.
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.
长期以来，机器人专家一直从自然界汲取灵感，以开发诸如RatSLAM之类的导航与同步定位及地图构建（SLAM）系统。鸟类和蝙蝠等动物拥有卓越的导航能力，能够在大型三维环境中稳健导航，利用空间的内部神经表征，结合外部感官线索和自身运动线索。本文提出了一种名为NeuroSLAM的新型神经启发式四自由度（4DoF）SLAM系统，该系统基于三维网格细胞和多层头部方向细胞的计算模型，并与提供外部视觉线索和自身运动线索的视觉系统相结合。NeuroSLAM的神经网络活动实时驱动创建多层图形体验地图，通过熟悉的局部视觉线索序列实现重新定位和闭环。使用多层体验地图松弛算法在闭环后校正路径积分中的累积误差。利用包含复杂多层室内和室外环境的合成数据集和真实世界数据集，我们证明NeuroSLAM始终能生成拓扑正确的三维地图。
