Brain-Inspired Multisensor Navigation Information Fusion Model Based on Spatial Representation Cells

The mammalian brain manages navigational behavior by processing sensory information. A brain-inspired multisensor navigation information fusion model is developed based on discovered neural mechanisms. The architecture of this model is inspired by the information transmission method of a part of the brain, which integrates navigation information from multiple sensors to provide the position of unmanned systems. First, the brain-inspired multisensor information fusion architecture is established based on the anatomical structure of hippocampal formation. Then, continuous attractor neural networks (CANNs) are used to model head-direction cells (HDCs), 3-D grid cells (GCs), and 3-D place cells (PCs). These spatial representation cell models integrate external perceptual information and self-motion cues to generate firing rates, which realizes navigation information fusion and accurate spatial cognition for unmanned systems. Finally, the methods of decoding the firing rates of these spatial representation cells are proposed to obtain navigation parameters. The proposed model is verified on the simulated data, the KITTI dataset, and the unmanned ground vehicle. The experiments demonstrate that the proposed brain-inspired model can fuse multisensor information, leading to more accurate positioning than traditional navigation models.