# Brain-Like Navigation Scheme based on MEMS-INS and Place Recognition
## 4 Experimental Test and Comparison
### Comparison of Algorithms  
Before the experiment is carried out, a ROI (region of interest) should be delimited first, therefore the computation cost can be further reduced, and the matching result would be more accurate and reliable. The area should include as many scenario features as possible on both sides of the road and try to avoid uncertainties such as pedestrians and vehicles on the road (the area inside the blue rectangle in Figure 12). In this experiment, the value of the threshold \(h\) of the SI algorithm is set as 0.075 by experiences, and the scanline profiles at the place where the nodes of place cells are located are shown as the following:  
The three similarity values \(\beta^{\prime}\) of the three nodes shown in Figure 13 are 0.069, 0.066 and 0.0704, respectively. Figure 11: The driven routes and the places of the nodes for visual matching. Figure 12: The ROI in this experiment. However, the misjudgment of place matching happens sometimes when the SI algorithm is used independently. For example, in experiment 1, a misjudgment happens, as shown in Figure 14. The calculated \(\beta^{\prime}\) value there is 0.0743 (Figure 14), which is smaller than the threshold \(h=0.075\). Figure 13: The captured images and their scanline profiles at the place where the three templates are captured. Therefore, the SI algorithm, which is only based on the scanline intensity profile, is considered not reliable, and would cause serious consequences in the actual application if misjudgment occurs. The scanline intensity of the acquired images is calculated and compared to the scanline intensity of the template images, and the acquired image would be retained, and its location would be thought of as a potential place cell node if the calculated similarity is less than 0.075. Then the position constraint is applied, which means that the potential node is matched properly only if the distance between the coordinate value given by the template image and INS is less than 100 m. After that, the template image and the acquired image are matched by using the GMS algorithm. The matching result is considered reliable if the number of matching points (\(N\)) that are successfully matched meets the constraint of \(N\geq\Omega\). The place cell is then firing to reset. The constraint of \(\Omega=75\) is set for this experiment, and a total of no more than 500 characteristic points are detected for a single image. The experimental results indicate that the proposed SI-GMS algorithm can effectively improve the recognition accuracy of place cells, as shown in Figure 15. From Figure 15 we can see that: at the nodes of the place cells, the numbers of feature points where the real-time acquired images successfully match the visual template are 115, 248 and 100 for experiment 1, and 97, 183 and 204 for experiment 2, respectively. Therefore, the three nodes could be considered as trustworthy nodes of place cells. Figure 14: The specific locations on the map, raw images and their scanline profiles where the misjudgment happened. (The value of \(g^{\prime}\) of these two images is 0.0743.). Figure 15: The result of the successfully matched feature points between real-time acquired images and template images by using the SI-GMS algorithm. (**a**) Experiment 1; (**b**) Experiment 2. However, as shown in Figure 16, at the misjudged location by the SI algorithm, only three characteristic points are successfully matched by using the GMS algorithm, which means that the misjudgments could be effectively avoided. By using LattePanda, the time of matching a pair of images only takes 53 ms by GMS. However, in practical applications, there would be many template images. This means that each acquired image is matched to multiple template images. Take the number of templates as 10 as an example, the matching time cost approximately 624 ms when using GMS algorithm, which is impossible to meet real-time requirements.