# Brain-Like Navigation Scheme based on MEMS-INS and Place Recognition
## 1 Introduction  
Navigation technology plays a significant role in our life. Even in an unfamiliar environment, we can easily get the current location and reach our destination with the help of GPS. Nevertheless, GPS may be blocked in some environments, such as the places between tall buildings in cities, deep-sea area, deep mountains and forests, etc. Micro-Electro-Mechanical System Inertial navigation system (MEMS-INS) demonstrates its unique superiority, which can calculate the next-point location based on the continuously measured self-motion velocity and direction information, rather than external information, so it has a vast application scope. Whereas, the navigation information of INS is generated by the integration of velocity and direction information measured by sensors, so the error will increase over time, thereby resulting in poor positioning accuracy over a long time [1; 2; 3]. Additionally, inertialsensors suffer from large measurement uncertainty at slow motion [4]. It has undoubtedly become one of the research hotspots to reasonably and effectively reduce these accumulated errors and improve the intelligence of the INS in recent years. Studies have shown that the navigation strategies which integrate one or more navigation means, such as GPS [5; 6; 7], Spectral Redshift [8], Polarized Light [9] etc., with INS can obtain complementary advantages and achieve better navigation results. The Unmanned Aerial Vehicle (UAV) has played an increasingly important role in various fields in recent years [10], to achieve better navigation results, GPS/INS is the most widely used integrated navigation system especially for UAVs [11; 12; 13]. However, in GPS blocked environments, the GPS/INS system would be shut down and go back to INS standalone mode and the navigation error would be accumulated [14]. As a common integrated navigation method for UAVs, visual navigation also has many drawbacks [15]. On the one hand, the performance of visual navigation is inversely proportional to the velocity. Another limitation of visual sensors is due to their inability to distinguish rotational from translational motion, since it requires the summation of six motion components. Moreover, the mentioned integrated navigation systems are still simply a combination of different devices without any intelligent ability. To endow INS with intelligent ability, the attention of researchers is turned to the biological world. Many animals have outstanding navigation ability, such as pigeon post, old horse knowing the way, etc. Whether across a myriad of rivers, thousands of hills, in a mightty storm or some other severe weather event, these amazing animals always know the way back home. Many people are good at recognizing the way too, and they seem to have a high-resolution map embedded in their mind so that they will not get lost somehow. It shows that a living organism can obtain the perception of external information just by its own organs, which can be transformed into accurate navigation information through a certain biological mechanism. The Nobel Prize in Physiology or Medicine in 2014 revealed this mystery for us. As early as in 1971, [PERSON] discovered a type of neuron in an encephalic region called hippocampus in the rat brain, which was responsible for remembering location features [16]. More than 30 years later, [PERSON] and [PERSON] demonstrated that there was a mechanism for establishing spatial coordinate system in an animal's brain, which could generate path integration by obtaining motion information from speed cells [17] and head-direction (HD) cells [18; 19]. In imitation of this mechanism, [PERSON] conducted the mathematical model of a rat brain, and established the SeqSLAM bionic navigation algorithm by using the vision-driven navigation system and achieved good results in outdoor navigation experiments [20]. SeqSLAM is a pure visual navigation algorithm, of which the core sensing information only come from the visual odometer.