# INS/Vision Integrated Navigation System Based on a Navigation Cell Model of the [PERSON]  
Correspondence: [EMAIL_ADDRESS] (C.S. ); [EMAIL_ADDRESS] (J.L. ); Tel. : +86-152-3410-2695 (C.S. ); +86-0351-3922457 (J.L.) Chenguang Wang  
Xiaoting Guo  
can obtain complementary advantages and achieve better navigation results, where GPS/INS is a typical integrated navigation system, especially for land vehicles. The system is heavily dependent on GPS, a passive navigation system, which is easily blocked or invalidated by human or external environmental factors [4]. As a result, the overall performance of the integrated navigation system may be affected. Due to using no radiation energy, working in all weather conditions, covering all areas and having low energy consumption, INS/geomagnetic integrated navigation systems have received wide attention and research [5]. However, the accumulated velocity and position errors cannot be eliminated fundamentally. Besides, if the local magnetic information is abnormal, the geomagnetic system itself introduces navigation errors. Additionally, the combination of an odometer, Doppler instrument, Wi-Fi, Ultra-Wideband (UWB) and INS has also been widely studied [6]. It is worth noting that by fusing the information from auxiliary sensors and the INS, the current integrated navigation systems do not have intelligent abilities. To endow a navigation system with intelligent abilities, the attention of researchers is turned to the biological world, and they learn from animals to study their intelligent navigation mechanisms. Carrier pigeons have an autonomous homing ability. Rats can always find their way home. Insects such as bees and sand ants can return to their nests hundreds of meters away along a straight line after complex and tortuous foraging processes in the absence of significant references [7]. Research results show that by perceiving surrounding environments, animals can obtain accurate navigation information. In 2014, the Nobel Prize in Physiology or Medicine was awarded to scientists who discovered the animal's brain navigation system [8]. They revealed that a type of neuron is responsible for location remembering, and is located in an encephalic region called the hippocampus of the rat brain. The main functional cells include speed cells and place cells. The above research results show that when animals move, they realize autonomous navigation by perceiving information in the external environment and combining it with internal cells [9]. Actually, when animals reach a familiar place, the path integrator of the brain will be reset to adapt to the environment information sensed by eyes, which means that the integrated position error can be corrected by the recognition of familiar places, which can be recognized as absolute position references. By capturing external artificial or natural information, vision navigation systems can realize the calculation of navigation parameters and achieve high precision during long journeys [10; 11; 12; 13]. In 2004, a bionic place recognition algorithm, named RatSLAM, was proposed. Then, SeqSLAM, which is the improvement of RatSLAM, was proposed in 2012 with higher recognition accuracy and wider range. However, the above vision navigation methods are not used for information fusion with INS data, and therefore cannot improve the intelligence degree of an INS. Inspired by the above research, this paper proposes a brain-like navigation scheme based on an INS/vision integrated navigation system. The system is intended to impart the environmental perception ability of the vision navigation system, and the intelligent navigation ability of the hippocampus navigation cell model, to the INS, and eventually endow the brain-like navigation system with strong autonomy, high intelligence and high reliability. The contributions of this paper are as follows:  
1. The INS/vision brain-like navigation model is proposed.