# INS/Vision Integrated Navigation System Based on a Navigation Cell Model of the [PERSON]
## 2 Brain-like Navigation Model
### INS/Vision Brain-like Navigation Model  
Biological research shows that single navigation cells cannot help animals to travel long distances. The process of sensing velocity by speed cells and integrating it to obtain position information will produce serious accumulation errors, which will make the travel farther and farther away from the destination during long-distance journeys [19]. The place cells can be used for location by identifying landmarks. However, the landmarks are not continuous and there is no fixed distance between them. Just relying on landmarks will lead to many detours. To accurately reach the destination, animals travelling far away combine multiple navigation cells together. The position information is obtained by the integral of velocity perception information from the speed cell. Also, the position error induced by the integral calculation can be corrected by a place cell by means of perception of the external landmark information. For a pure INS, the attitude, velocity and position information can be obtained by an integral using the measured angular velocity and acceleration. As navigation information is obtained by integration, the error increases with time, resulting in the gradual decrease of navigation accuracy. To reduce the error accumulation, and inspired by animal navigation cell models, we propose the brain-like navigation model based on an INS and vision navigation. Among them, the INS module is similar to the speed cell, used to obtain the position information by the integral of the measured velocity. The vision navigation system is similar to the place cell, using scene matching to identify the landmark and to further correct the position accumulation error of the INS. The hardware system of visual navigation based on a LattePanda development board and the INS based on an STM32 are also built, as shown in Figure 3. The specific procedure is to transplant the image matching algorithm into the LattePanda development board. The vision navigation system matches the real-time acquisition images with the pre-stored images. When the match is successful, the place information of the pre-stored image, as the current place information, is fed back to the INS through serial communication to correct the INS system. Then, the INS computation process renews at the current place. At the same time, the INS path integrator is compared with the visual matching place node to consider the sources of error. Based on this, the path integral is adjusted to reduce the errors, as shown in Figure 4. By using this method, the accumulated errors of the INS can be effectively eliminated, and the accuracy of navigation can be improved. Figure 2: Place cells. With the camera installed onto a vehicle, if the relative height and inclination of the camera are the same as the ground, for two different images taken at the same location, it can be regarded as a horizontal deviation, but there is no vertical deviation. Therefore, the region of interest of the template image can be moved from the reference image, which is the similarity of the corresponding relationship. In this way, the region of the two images after each shift can be calculated, and the minimum of all similarity values can be taken as the final similarity of the two images. To determine place cell nodes, distinctive landmarks are considered, such as symbolic buildings, crossroads, and so on. As sufficient feature points can be used in such scenarios, they are often selected as place cell nodes. Another reason is that images in such scenarios can be obtained not only by precapturing but also from an online picture database. Then, the oriented FAST and rotated BRIEF (ORB) algorithm extracts feature points from video images and tries to match the video image with the template image. The feature points have the information of size and orientation. By matching feature points between the video image and the template image, the distance of the matched points can be determined. The minimum distance of matched points is set as the Hamming distance.