# Brain-Like Navigation Scheme based on MEMS-INS and Place Recognition
## 1 Introduction
In fact, when animals or humans reach a familiar environment, the path integrator will be reset to adapt to the external environmental information sensed by the eyes. It means that the integrated position error can be corrected when animals or humans come into a familiar environment, where it can be recognized as an absolute position reference, therefore, the navigation accuracy can be remarkably enhanced by intermittently eliminating accumulated errors. The crux of stimulating such error correction mechanism of organisms lies in whether it is capable of rapidly and accurately matching current visual scenes with the feature scenario in \"memory.\" SeqSLAM uses an algorithm based on image grayscale information to conduct closed-loop detection of scenes. It's advantages include the small computing amount and excellent real-time, but it has poor robustness and is vulnerable to environmental factors. By contrast, other visual matching algorithms such as Vector Field Consensus algorithm (VFC) [21], Guided Locality Preserving Matching algorithm (GLPM) [22] and Grid-based Motion Statistics algorithm (GMS) [23] are based on feature points. Most of these algorithms usually perform feature extraction first [24; 25], and then match the extracted feature points in some way [26]. Therefore, they usually have highly accurate matching results, but the computation cost still needs to be improved for real-time application. To effectively eliminate the accumulative position errors of INS and enhance its intelligence, this paper first investigates the navigation mechanism of rats. The investigation finds that the reason why rats can eliminate the accumulative errors caused by path integration is that they have certain cognitive competence about the environment. Inspired by this, this paper combines vision and INS together, putting forward a brain-like navigation scheme. This system can recognize \"spots in memory\" during movement to perform error correction for INS at those spots. Additionally, to better solve the place recognition problem of the scheme, this paper compares and analyzes traditional grayscale based matching methods with feature point based matching methods, and at last proposes a place recognition algorithm which is named SI-GMS. Experimental results suggest the scheme mentioned above can considerably improve the positioning accuracy of INS. The main contributions of this paper are summarized as follows:  
The paper presents a \"brain-like navigation\" scheme based on the principle of rat brain navigation cells. The proposed brain-like navigation scheme corrects position errors caused by path integration through visual information, which improves the position measurement precision and intelligence level of INS. The paper introduces a place recognition algorithm that integrates gray information and feature point information of images specific to the proposed brain-like navigation scheme. This algorithm has numerous advantages including real-time and accurate matching results. It can effectively overcome the shortcomings of the traditional place recognition algorithms, such as missed judgement, misjudgment and poor environmental suitability. The paper is organized as follows: Section 2 is the proposed brain-like navigation model. Section 3 is the description of proposed place matching algorithm. Experiment and comparison results are presented in Section 4. The paper ends with a conclusion in Section 5.