# Brain-Like Navigation Scheme based on MEMS-INS and Place Recognition
## 5 Conclusions  
This paper has proposed a brain-like navigation system scheme to improve the intelligence level of the INS. Compared with pure INS, by incorporating visual information, the scheme can effectively eliminate the accumulated error of INS. Aiming at the place recognition problem in the scheme, this paper also proposes a place recognition algorithm that takes both image gray information and image feature points information into consideration. Experimental results demonstrate that this algorithm is superior to the traditional image matching algorithm in terms of calculation speed and accuracy, and this scheme is also practical and reliable.  
As for future work, more sensors can be introduced into the system for further promotion, like polarized light and magnetic sensors. In order to save time when creating the template image database, we are trying to replace the existing database with image data from Google Earth. We also consider using the position error information to refine the equivalent biases of the gyroscopes and accelerometers, which is a major source of errors in the unconstrained IMU integration. Besides, we are going to perform more longer experiments that include indoor complex areas to test the algorithm further.  
\begin{table}
\begin{tabular}{l c c c c} \hline  & **High Precision GPS** & **Pure INS** & **Brain-Like Navigation Scheme** & **SeqSLAM** \\ \hline North latitude (\({}^{\circ}\)) & 38.016407 & 38.016305 & 38.016394 & 38.016269 \\ \hline East longitude (\({}^{\circ}\)) & 112.44755 & 112.44756 & 112.44758 & 112.44771 \\ \hline \end{tabular}
\end{table}
Table 6: Coordinates at terminus of experiment 2.  
\begin{table}
\begin{tabular}{l c c c} \hline  & **Pure INS** & **SeqSLAM** & **Brain-Like Navigation Scheme** \\ \hline Error of expt.1(m/500 m) & 37.2 & 53.1 & 8.4 \\ \hline Error of expt.2(m/500 m) & 22.8 & 56.4 & 6.2 \\ \hline \end{tabular}
\end{table}
Table 7: Distance error.  
\begin{table}
\begin{tabular}{l c c c c} \hline  & **High Precision GPS** & **Pure INS** & **Brain-Like Navigation Scheme** & **SeqSLAM** \\ \hline North latitude (\({}^{\circ}\)) & 38.017588 & 38.017921 & 38.017513 & 38.018032 \\ \hline East longitude (\({}^{\circ}\)) & 112.44490 & 112.44493 & 112.44489 & 112.44489 \\ \hline \end{tabular}
\end{table}
Table 5: Coordinates at terminus of experiment 1.  
**Author Contributions:** [PERSON] and [PERSON] contributed equally to this paper. Conceptualization, [PERSON], [PERSON]; methodology, [PERSON], H.H.; software, [PERSON], X.C., H.C.; writing--original draft preparation, Y.Z., J.T.  
**Funding:** This work was supported in part by the National Natural Science Foundation of China (61603353, 51705477, 51705021, U1764261, 91420203, 61703098), and the Pre-research Field Foundation (6140518010201) and the Scientific and Technology Innovation Programs of Higher Education Institutions in Shanxi (201802084), and the Shanxi Province Science Foundation the Pre-research Field Foundation (6140518010201) and the Scientific and Technology Innovation Programs of Higher Education Institutions in Shanxi (201802084), the Shanxi Province Science Foundation for Youths (201601D021067), the Program for the Top Young Academic Leaders of Higher Learning Institutions of Shanxi, the Young Academic Leaders Foundation in North University of China, Science Foundation of North University of China (XJJ201822), the Fund for Shanxi \"1331 Project\" Key Subjects Construction, Natural Science Foundation of Jiangsu Province under Grant BK20160699.  
**Conflicts of Interest:** The authors declare no conflict of interest.