# Brain-Like Navigation Scheme based on MEMS-INS and Place Recognition
## 2 Proposed Solution
### Proposed Brain-Like Navigation Model Based on Vision and INS
\begin{array}{c}\left[\begin{array}{c} \Delta\vec{e_{0}}\vec{\
u}\cos\theta\\ \vec{e_{0}}\vec{\
u}\sin\theta\end{array}\right]\\ \end{array} \right] \tag{7}\]where \(\lfloor\) \(\rfloor\) refers to rounding down, \(C_{i}\) and \(C_{j}\) are the constants for path integration; \(v\) is the current velocity which is achieved from speed cells; \(\theta\) is the current head direction which is achieved from HD cells, and \(\overrightarrow{e_{\theta}}\) is the unit vector pointing at \(\theta\). Similar to the mechanism of biological autonomous navigation, the INS is a type of autonomous system that neither relies on external information nor radiates energy outwards. By measuring the angular rate and acceleration of the carrier in the inertial frame of reference, the navigation information such as speed, yaw angle and position within the navigation coordinate system can be obtained after calculation. Nonetheless, error exists in the system output, and the error will be accumulatively increasing due to the fact that the position of INS is obtained via integral. Regarding living rats, the place cells' relative position of firing is also obtained by path integration. However, when a rat comes into a familiar environment, rats will reset the firing for all the spatial cells involved in the path integration. The renewal of coordinates is in effect the renewal process of spatial cells' firing, whereas the renewal process at the close-loop point is the reset process of spatial cells. Based on this mechanism, the brain-like navigation model based on vision and INS is proposed. This system is capable of conducting real-time detection on whether the current visual information is matched with the pre-stored visual template. If it is successfully matched, it means that a \"familiar place\" is reached. Subsequently, the spatial cells within the entire path integration network will undergo a firing reset procedure, so as to regain the firing state of the previous close-loop point. Via this method, the accumulated errors can be effectively eliminated, and the navigation accuracy of INS will be increased. The model overview is as follows:  
```
1. The camera captures a frame of RGB image
2. Collect the motion state information of the object, and renew speed cells and HD cells
3. Execute path integration of spatial cell
4. Obtain spatial geometric coordinates via geometric transformation
5. Execute the image matching algorithm and obtain the return value \(U\) of the matching result using (19)
6. If \(U==True\)
7. Read the location coordinates of the template image
8. Reset the firing for place cells
9. Correct the position errors
10. End if
```  
**Algorithm 1** The model overview of INS  
The schematic diagram of the proposed brain-like navigation scheme is shown in Figure 4. The real trajectory of the UAV is a straight blue line that starts from point A, followed by point B, point C and point D. However, due to the accumulated errors, the trajectory of INS will gradually deviate from the real track. The red dots are visually corrected points, at which the place cells are reset. The brown dotted line represents the reckoned trajectory of the UAV and the purple line represents the trajectory of pure INS. It can be found that the trajectory of INS after visual correction is closer to the real trajectory of the UAV, and the navigation accuracy has also been significantly improved. The brain-like navigation scheme studied in this paper is mainly applied to UAV. However, the landscape features near the ground are complicated due to various interference factors. The key to realize the proposed scheme is a fast and accurate place recognition algorithm. Therefore, this paper proposes a fast and accurate place recognition algorithm for the proposed brain-like navigation system in the next section.