Mammals rely on vision and self-motion information in nature to distinguish directions and navigate accurately and stably. Inspired by the mammalian brain neurons to represent the spatial environment, the brain-inspired positioning method based on multi-sensors' input is proposed to solve the problem of accurate navigation in the absence of satellite signals. In the research related to the application of brain-inspired engineering, it is not common to fuse various sensor information to improve positioning accuracy and decode navigation parameters from the encoded information of the brain-inspired model. Therefore, this paper establishes the head-direction cell model and the place cell model with application potential based on continuous attractor neural networks (CANNs) to encode visual and inertial input information, and then decodes the direction and position according to the population neuron firing response. The experimental results confirm that the brain-inspired navigation model integrates a variety of information, outputs more accurate and stable navigation parameters, and generates motion paths. The proposed model promotes the effective development of brain-inspired navigation research.
Keywords: brain-inspired navigation; continuous attractor neural networks (CANNs); head-direction cells; place cells; population neuron decoding.